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Artículos de revistas sobre el tema "Modelli diagnostici ML"

Autor: Grafiati

Publicado: 10 de marzo de 2023

Última modificación: 31 de julio de 2025

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Bhore, Prof Priyanka. "Advancing Diagnostic Accuracy and Efficiency through Machine Learning Integration in Healthcare." International Journal for Research in Applied Science and Engineering Technology 13, no. 1 (2025): 1967–73. https://doi.org/10.22214/ijraset.2025.66707.

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Machine learning (ML) has the potential to transform healthcare by improving the accuracy and efficiency of medical diagnoses. This project showcases the use of ML in healthcare through a DenseNet121 model designed to classify chest X-ray images into four categories: Pneumonia, Atelectasis, Pneumothorax, and No Finding. Utilizing the DenseNet121 architecture, recognized for its strong feature extraction abilities, the model was trained on a dataset of chest X-ray images along with relevant metadata. The goal was to accurately identify these conditions, thereby assisting healthcare professional

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Özer, İlyas. "Utilizing Machine Learning for Enhanced Diagnosis and Management of Pediatric Appendicitis: A Multilayer Neural Network Approach." Aintelia Science Notes 2, no. 2 (2023): 18–24. https://doi.org/10.5281/zenodo.10473089.

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This study focuses on pediatric appendicitis, a leading cause of hospital admissions due to abdominal pain in children, characterized by a substantial risk of perforation, especially in younger patients. Traditional diagnostic methods, while effective, often lack specificity and are supplemented by varying laboratory and imaging techniques. This research introduces a novel application of machine learning (ML), specifically a multi-output neural network model, to address the complexities of diagnosing appendicitis, determining its severity, and guiding management strate

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Thamodharan, A. "Advanced Predictive Modeling for Early Detection of Diabetes Insipidus: Leveraging Machine Learning Algorithms to Enhance Diagnostic Accuracy and Personalized Treatment Pathways." International Scientific Journal of Engineering and Management 04, no. 04 (2025): 1–7. https://doi.org/10.55041/isjem03046.

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Abstract: Diabetes Insipidus (DI) is a rare disorder characterized by the inability to concentrate urine, leading to frequent urination and excessive thirst. Early detection of DI is crucial for timely treatment, as delayed diagnosis can result in complications such as dehydration, electrolyte imbalances, and kidney damage. This paper explores the application of advanced predictive modeling techniques, particularly machine learning (ML) algorithms, to enhance the early detection and diagnosis of Diabetes Insipidus. Traditional diagnostic approaches, such as water deprivation tests and serum os

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Ding, Yueheng. "Advances and Challenges in Machine Learning for Diabetes Prediction: A Comprehensive Review." Applied and Computational Engineering 109, no. 1 (2024): 75–80. http://dx.doi.org/10.54254/2755-2721/109/20241437.

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Abstract. Diabetes mellitus is a prevalent and severe metabolic disorder disease that poses significant health risks globally, leading to substantial healthcare burdens. Recent days, advancements in artificial intelligence (AI) have markedly enhanced the accuracy and efficiency of diabetes outcome predicted by machine learning (ML), offering a promising approach for early intervention and treatment. This paper evaluates several advanced ML models, including Random Forest (RF), Support Vector Machine (SVM), and Neural Networks techniques based on neural networks. Each model's strengths and limi

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Kadhim, Dhuha Abdalredha, and Mazin Abed Mohammed. "Advanced Machine Learning Models for Accurate Kidney Cancer Classification Using CT Images." Mesopotamian Journal of Big Data 2025 (January 10, 2025): 1–25. https://doi.org/10.58496/mjbd/2025/001.

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Kidney cancer, particularly renal cell carcinoma (RCC), poses significant challenges in early and accurate diagnosis due to the complexity of tumor characteristics in computerized tomography (CT) images. Traditional diagnostic approaches often struggle with variability in data and lack the precision required for effective clinical decision-making. This study aims to develop and evaluate machine learning (ML) models for the accurate classification of kidney cancer using CT images, focusing on improving diagnostic precision and addressing potential challenges of overfitting and dataset heterogen

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Al-Batah, Mohammad, Mowafaq Salem Alzboon, and Muhyeeddin Alqaraleh. "Superior Classification of Brain Cancer Types Through Machine Learning Techniques Applied to Magnetic Resonance Imaging." Data and Metadata 4 (January 1, 2025): 472. http://dx.doi.org/10.56294/dm2025472.

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Brain cancer remains one of the most challenging medical conditions due to its intricate nature and the critical functions of the brain. Effective diagnostic and treatment strategies are essential, particularly given the high stakes involved in early detection. Magnetic Resonance (MR) imaging has emerged as a crucial modality for the identification and monitoring of brain tumors, offering detailed insights into tumor morphology and behavior. Recent advancements in artificial intelligence (AI) and machine learning (ML) have revolutionized the analysis of medical imaging, significantly enhancing

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Hasan, Aseel, and Mahdi Mazinani. "DETECTION OF KERATOCONUS DISEASE DEPENDING ON CORNEAL TOPOGRAPHY USING DEEP LEARNING." Kufa Journal of Engineering 16, no. 1 (2025): 463–78. https://doi.org/10.30572/2018/kje/160125.

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Keratoconus is a disease that ML has contributed much in its diagnosis and management. It is not a widely prevalent disease, with a research gap caused by the absence of standardized datasets for model training and evaluation. This work presents a novel dataset, which strengthens the CNN model's resilience and creates standards for assessing keratoconus diagnostic techniques. The research depends on data of patients examined at Jenna Ophthalmic Center in Baghdad. The proposed system works on three stages: pre-processing, feature extraction, and classification with machine learning algorithms i

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Patel, Sumir, Veysel Kocaman, Mehmet Burak Sayici, and Nikhil Patel. "Auto-machine learning for opportunistic thyroid nodule detection in lung cancer screening chest CT." Journal of Clinical Oncology 42, no. 16_suppl (2024): e13639-e13639. http://dx.doi.org/10.1200/jco.2024.42.16_suppl.e13639.

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e13639 Background: Automated Machine Learning (Auto-ML) in medical imaging is a process that allows non-experts to utilize machine learning techniques, opening the door for non-coder physician-driven exploitation of the technology. Auto-ML was applied for opportunistic detection of thyroid nodules in the context of low-dose lung cancer screening chest CT, facilitated by an innovative platform integration. By leveraging scans originally intended for lung cancer screening, suspicious appearing asymptomatic thyroid nodules can also be screened for where technically feasible. Methods: CT scans fro

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Kumar Singh, Siddhanta, and Anand Sharma. "Revving up insights: machine learning-based classification of OBD II data and driving behavior analysis using g-force metrics." Bulletin of Electrical Engineering and Informatics 14, no. 3 (2025): 2188–97. https://doi.org/10.11591/eei.v14i3.9398.

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This research work uses machine learning (ML) approaches to classify on-board diagnostics II (OBD II) data and g-force measures to provide a thorough analysis of driving behavior. The research paper effectively demonstrates the classification of driving behaviours using OBD II and g-force data. Driving behaviours are analyzed by using ML algorithms such as random forest (RF), AdaBoost, and K-nearest neighbors (KNN). The analysis goes beyond a summary by discussing how OBD II data, g-force metrics, and the algorithms interrelate to classify ten distinct driving behaviors (e.g., weaving, swervin

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S, Suresh, and Dhanalakshmi S. "Tuberculosis prediction: performance analysis of machine ‎learning models for early diagnosis and screening using ‎symptom severity level data." International Journal of Basic and Applied Sciences 14, no. 1 (2025): 435–44. https://doi.org/10.14419/parmkr90.

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Tuberculosis (TB) remains a formidable issue for worldwide public health and calls for swift and exact diagnostic strategies to achieve the ‎best health results for those affected. A methodical machine learning (ML) sequence was diligently followed, featuring data preprocessing, ‎feature choice, encoding, and the training of the model in a logical order. A detailed investigation was performed on six unique machine ‎learning architectures, comprising the ANN, SVM, Decision Tree, Random Forest, XGBoost, and Logistic Regression, closely analyzing ‎their key performance measures essential for meas

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Dingli, David, Darlyna Khonkhammy, Caden Gunnarson, Zach Morena, Dan Que, and Ali Khammanivong. "Diagnosis of AL Amyloidosis Versus Monoclonal Gammopathy of Undetermined Significance and Smoldering Multiple Myeloma Using FTIR/ML Analysis of Serum." Blood 144, Supplement 1 (2024): 3296. https://doi.org/10.1182/blood-2024-194736.

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The prognosis of patients with immunoglobulin light or heavy chain amyloidosis (AL/AH) has improved with the advent of more effective therapies. However, diagnosis is often delayed, with a negative impact on overall survival due to irreversible end-organ damage. Therefore, technologies that could facilitate the diagnosis or raise early suspicion of AL/AH amyloidosis in patients would be beneficial to improve outcomes. We hypothesized that Fourier transform infrared spectroscopic analysis of serum combined with machine learning (FTIR/ML) may be able to correctly diagnose AL/AH amyloidosis compa

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Noviandy, Teuku Rizky, Ghifari Maulana Idroes, Maimun Syukri, and Rinaldi Idroes. "Interpretable Machine Learning for Chronic Kidney Disease Diagnosis: A Gaussian Processes Approach." Indonesian Journal of Case Reports 2, no. 1 (2024): 24–32. http://dx.doi.org/10.60084/ijcr.v2i1.204.

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Chronic Kidney Disease (CKD) is a global health issue impacting over 800 million people, characterized by a gradual loss of kidney function leading to severe complications. Traditional diagnostic methods, relying on laboratory tests and clinical assessments, have limitations in sensitivity and are prone to human error, particularly in the early stages of CKD. Recent advances in machine learning (ML) offer promising tools for disease diagnosis, but a lack of interpretability often hinders their adoption in clinical practice. Gaussian Processes (GP) provide a flexible ML model capable of deliver

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Ahmad Qureshi, Muhammad Danial, Muhammad Fahid Ramzan, Fatima Amjad, and Naeem Haider. "Artificial Intelligence in Metabolomics for Disease Profiling: A Machine Learning Approach to Biomarker Discovery." Indus Journal of Bioscience Research 2, no. 2 (2024): 87–96. http://dx.doi.org/10.70749/ijbr.v2i02.146.

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With an emphasis on the identification of biomarkers for pancreatic cancer, this research investigated the use of artificial intelligence (AI) and machine learning (ML) in metabolomics for disease profiling. With the use of the Kaggle dataset "Pancreatic Cancer Urine Biomarkers," which comprises 591 samples from diverse patient cohorts, we examined the connections between distinct proteome and metabolomic markers and their diagnostic value. Plasma CA19-9, LYVE1, REG1B, REG1A, and TFF1 were among the key biomarkers that were assessed in order to create a prediction model that could differentiat

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Miriyala, Geetha Pratyusha, and Arun Kumar Sinha. "Precision Diagnosis of Coronary Artery Disease with OTLGBM." Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications 16, no. 1 (2025): 230–46. https://doi.org/10.58346/jowua.2025.i1.014.

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Coronary Artery Disease (CAD) causes the highest number of deaths worldwide, determining the early need for accurate diagnostic methods. This paper proposes an improved method for diagnosing CAD using Machine Learning models. The methodology aims to enhance diagnosis with the Optimal Tuned Light Gradient Boosting Machine (OT-LGBM) using Bayesian optimization. In addition to the optimization, the feature selection with the LGBM is incorporated into the framework for improving the model's prediction accuracy. The selected important features are trained with the model, and the optimization model

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Chibueze, K. I., A. F. Didiugwu, N. G. Ezeji, and N. V. Ugwu. "A CNN based model for heart disease detection." Scientia Africana 23, no. 3 (2024): 429–42. http://dx.doi.org/10.4314/sa.v23i3.38.

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Cardiovascular diseases (CVDs) pose a formidable global health challenge, claiming millions of lives annually. Despite advancements in healthcare, heart disease remains a leading cause of mortality, especially in developing nations. Early detection of cardiac abnormalities through predictive models is crucial for effective intervention. This research leverages machine learning (ML) and artificial intelligence (AI), focusing on deep learning, to enhance diagnostic capabilities. Unlike previous studies, this work introduced caffeine as a potential risk factor often overlooked in datasets. The st

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Revanth Sankul, Greeshma Arrapogula, Sai Varun Kankal, Tejaswi Reddy Aruva, and Shoeib Khan Mohammed. "An optimized framework for brain tumor detection and classification using deep learning algorithms." World Journal of Advanced Engineering Technology and Sciences 15, no. 2 (2025): 687–96. https://doi.org/10.30574/wjaets.2025.15.2.0601.

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Brain tumors are among the most critical and life-threatening diseases, requiring early and accurate diagnosis for effective treatment. Traditional diagnostic methods rely on manual assessment of medical images, which can be time-consuming and prone to human error. This study presents an automated approach for brain tumor detection and classification using deep learning and texture analysis techniques. A convolutional neural network (CNN) is employed for feature extraction and classification, while texture analysis methods, such as Gray Level Co-occurrence Matrix (GLCM) and Local Binary Patter

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Basaligheh, Parvaneh, and Ritika Dhabliya. "Precision Agriculture through Deep Learning Algorithms for Accurate Diagnosis and Continuous Monitoring of Plant Diseases." Research Journal of Computer Systems and Engineering 4, no. 2 (2023): 31–45. http://dx.doi.org/10.52710/rjcse.72.

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For sustainable food production, precision agriculture is essential, and one of its main tenets is the precise identification and ongoing surveillance of plant diseases. Conventional approaches to illness monitoring and detection are frequently labour-intensive, time-consuming, and dependent on visual inspection, which increases the risk of misidentifying diseases. Deep learning algorithms have surfaced as a potentially effective way to tackle these issues. In this study, we introduce a novel method for precision agriculture that improves plant disease diagnostic accuracy and offers continuous

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Vianello, A. "Digital Health in clinical practice: an example of an expert system for heart failure management." Journal of AMD 28, no. 1-2 (2025): 91. https://doi.org/10.36171/jamd25.28.1-2.9.

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Sistemi di Supporto alla Decisione Clinica (Clinical Decision Support Systems, CDSS) sono strumenti fondamentali per assistere i medici nel processo decisionale, grazie alla loro capacità di analizzare dati clinici e fornire raccomandazioni diagnostiche o terapeutiche. In letteratura, questi sistemi sono classificati principalmente in due categorie: quelli basati sulla conoscenza, che utilizzano regole logiche di tipo IF-THEN fondate sull’esperienza clinica degli esperti, e quelli basati sull’apprendimento automatico (Machine Learning, ML), che sfruttano modelli statistici per identificare

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Malek, Ehsan, Juan Pablo Capdevila, Jennifer M. Ahlstrom, et al. "Speeding up Myeloma Care: Robust Machine Learning Predictions of M-Spike Levels." Blood 144, Supplement 1 (2024): 7604. https://doi.org/10.1182/blood-2024-200888.

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Introduction: Multiple myeloma (MM), a malignancy of plasma cells, poses diagnostic challenges due to its complex manifestations and variable outcomes. Accurate and rapid assessment of M-spike values is crucial for monitoring disease management . The gold standard for monitoring MM treatment response is serum and urine protein electrophoresis, which quantifies M-spike proteins; however, the turnaround time for results is 3-7 days, delaying treatment decisions. We hypothesized that machine learning (ML) could integrate structured electronic clinical and laboratory data (EHR) to rapidly and accu

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Pérez Míguez, Carlos, Jose Angel Diaz Arias, Jose Antonio Taibo Salorio, et al. "Smartcytoflow: A Machine Learning Decision Support System for Flow Cytometry Analysis in Non-Hodgkin Lymphoma Diagnosis and Screening." Blood 144, Supplement 1 (2024): 3606. https://doi.org/10.1182/blood-2024-199000.

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Introduction Flow cytometry is essential for diagnosing lymphoid neoplasms, offering detailed cellular profiles via specific antibody panels. However, manual analysis is time-consuming and variable. Machine learning (ML), using a self-organized map (SOM) and random forest classifier, can enhance diagnostic accuracy and efficiency. This study explores how ML can improve lymphoma diagnosis using flow cytometry data. Objectives The primary objective of this study was to develop and validate a machine learning (ML) model for accurately diagnosing non-Hodgkin lymphoma (NHL) from flow cytometry data

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Journal, IJSREM. "PARKINSON’S DISEASE PREDICTION USING MACHINE LEARNING." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 12 (2023): 1–13. http://dx.doi.org/10.55041/ijsrem27762.

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Parkinson's disease (PD) is a condition that affects a substantial number of individuals worldwide, leading to impairments in motor functions and a decline in overall quality of life. The timely identification of PD is vital for effective intervention and better patient outcomes. This study investigates the Utilization of machine learning methods in predicting and diagnosing Parkinson's disease using clinical and biomedical data.The dataset is pre-processed to address missing values and standardize features. Subsequently, various ML algorithms, including Support Vector Machines (SVM) are emplo

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Tuama, Murteza Hanoon. "A Comparative Evaluation of Random Forest and XGBoost Models for Disease Detection Using Medical Indicators." International Journal of Professional Studies 19, no. 1 (2025): 11–18. https://doi.org/10.37648/ijps.v19i01.002.

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In the field of 21st-century medicine, Machine Learning (ML) has become the cornerstone of healthcare by transforming disease detection at an earlier stage by facilitating early-stage accurate examination of medical attributes of the individuals including cholesterol, blood pressure and heart rate levels. Here we present an added comparison of two of the stratum of ensemble learning learned models: Random Forest and XGBoost on real-life medical datasets. The performance of the models was compared using Accuracy, Precision, Recall, F1-Score, and ROC-AUC. Random Forest showed great stability wit

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Innocent, Chukwudi Ekuma, Ihebuzo Ndubuka Gideon, Oladimeji Azeez Taofik, et al. "IMPLEMENTATION OF MACHINE LEARNING ALGORITHM FOR CARDIAC ARREST PREDICTION." Engineering and Technology Journal 08, no. 02 (2023): 1967–73. https://doi.org/10.5281/zenodo.7631909.

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Machine learning (ML) is a subfield of AI that uses statistical algorithms. Cardiac Arrest or heart failure has been implicated as one of the leading causes of death. The limited accuracy and the inherent invasiveness in diagnosis of this disease call for a revamp of the existing diagnostic protocol.  In this study, we developed Machine learning (ML) algorithms for the prediction of cardiac arrest. Our protocol employs different methods for classification of the HD dataset using univariate and Bivariate analysis for prediction of cardiac arrest on input data which contains 11 features suc

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Babačić, Haris, Nashif Mahruf Chowdhury, Mattias Berglund, et al. "Plasma Proteomics Differentiates between Patients with B-Cell Lymphomas." Blood 144, Supplement 1 (2024): 4370. https://doi.org/10.1182/blood-2024-204083.

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Introduction Techniques for assessing the blood plasma proteome with high precision and at great depth are rapidly developing and have demonstrated utility in carrying diagnostic and prognostic information for patients with cancer, including hematological malignancies. However, it is not known whether the plasma proteome can be useful in distinguishing the more closely related cancer entities, such as different B-cell lymphomas (BCLs). Performing affinity-based plasma proteomics analyses in a population-based cohort of BCLs, we aimed at discovering plasma proteome differences between BCL subty

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Trost, D. C., E. A. Overman, J. H. Ostroff, W. Xiong, and P. March. "A Model for Liver Homeostasis Using Modified Mean-Reverting Ornstein–Uhlenbeck Process." Computational and Mathematical Methods in Medicine 11, no. 1 (2010): 27–47. http://dx.doi.org/10.1080/17486700802653925.

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Short of a liver biopsy, hepatic disease and drug-induced liver injury are diagnosed and classified from clinical findings, especially laboratory results. It was hypothesized that a healthy hepatic dynamic equilibrium might be modelled by an Ornstein–Uhlenbeck (OU) stochastic process, which might lead to more sensitive and specific diagnostic criteria. Using pooled data from healthy volunteers in pharmaceutical clinical trials, this model was applied using maximum likelihood (ML) methods. It was found that the exponent of the autocorrelation function was proportional to the square root of time

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Mohd Radzol, Afaf Rozan, Y. Lee Khuan, Shyan Wong Peng, Irene Looi, and Wahidah Mansor. "Surface-enhanced raman spectroscopy with machine learning in non-invasive detection of dengue-NS1 fingerprint." ESTEEM Academic Journal 20, March (2024): 65–81. http://dx.doi.org/10.24191/esteem.v20imarch.616.g534.

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The surface-enhanced Raman spectroscopy (SERS) method exploits the plasmonic effect of nano-sized metallic materials to intensify the Raman scattering of the monochromatic light of analyte molecules. This promotes the sensitivity and specificity of the Raman spectroscopy analysis method. This study integrated SERS with machine learning (ML) to detect dengue fever, a disease infecting more than 40% of the world’s population. Non-structural protein 1 (NS1), detected in the sera of infected dengue patients during the early infection stage, is currently recognised as a biomarker for the early diag

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Dr.Risham, Fatima Pirzada Dr.Nadeem Khan Dr.Ayisha Afzal. "STUDY TO KNOW THE INCIDENCE OF INSULIN RESISTANCE IN POLYCYSTIC OVARIAN SYNDROME PATIENTS." INDO AMERICAN JOURNAL OF PHARMACEUTICAL SCIENCES 05, no. 08 (2018): 7970–74. https://doi.org/10.5281/zenodo.1403817.

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<strong><em>Objective:</em></strong><em> To determine the incidence of insulin resistance (IR) in polycystic ovarian syndrome patients with simple insulin resistance indices and to know the relation of PCOS clinical manifestations with insulin resistance indices.</em> <strong><em>Study Design: </em></strong><em>A cross-sectional study.</em> <strong><em>Place and Duration: </em></strong><em>In the Endocrinology Department of Services hospital, From December 2016 to December 2017 for duration of one year.</em> <strong><em>Methodology:</em></strong><em>. A hundred patients who met the Rotterdam 2

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Airlangga, Gregorius. "Optimizing Machine Learning Models for Urinary Tract Infection Diagnostics: A Comparative Study of Logistic Regression and Random Forest." Jurnal Informatika Ekonomi Bisnis, March 31, 2024, 246–50. http://dx.doi.org/10.37034/infeb.v6i1.854.

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Urinary Tract Infections (UTIs) present a significant healthcare challenge due to their prevalence and diagnostic complexity. Timely and accurate diagnosis is critical for effective treatment, yet traditional methods like microbial cultures and urinalysis are often slow and inconsistent. This study introduces machine learning (ML) as a transformative solution for UTI diagnostics, particularly focusing on logistic regression and random forest models renowned for their interpretability and robustness. We conducted a meticulous hyperparameter tuning process using a rich dataset from a clinic in N

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van Oosterzee, Anna. "AI and mental health: evaluating supervised machine learning models trained on diagnostic classifications." AI & SOCIETY, August 2, 2024. http://dx.doi.org/10.1007/s00146-024-02012-z.

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AbstractMachine learning (ML) has emerged as a promising tool in psychiatry, revolutionising diagnostic processes and patient outcomes. In this paper, I argue that while ML studies show promising initial results, their application in mimicking clinician-based judgements presents inherent limitations (Shatte et al. in Psychol Med 49:1426–1448. https://doi.org/10.1017/S0033291719000151, 2019). Most models still rely on DSM (the Diagnostic and Statistical Manual of Mental Disorders) categories, known for their heterogeneity and low predictive value. DSM's descriptive nature limits the validity of

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Siddhanta, Kumar Singh, and Sharma Anand. "Revving up insights: machine learning-based classification of OBD II data and driving behavior analysis using g-force metrics." May 19, 2025. https://doi.org/10.11591/eei.v14i3.9398.

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This research work uses machine learning (ML) approaches to classify on board diagnostics II (OBD II) data and g-force measures to provide a thorough analysis of driving behavior. The research paper effectively demonstrates the classification of driving behaviours using OBD II and g force data. Driving behaviours are analyzed by using ML algorithms such as random forest (RF), AdaBoost, and K-nearest neighbors (KNN). The analysis goes beyond a summary by discussing how OBD II data, g-force metrics, and the algorithms interrelate to classify ten distinct driving behaviors (e.g., weaving, swervin

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Manjunath, Chinthakunta, Archana Sasi, Smitha Chowdary Ch, et al. "Enhancing Glaucoma Detection in Fundus Images: A ResNet based Segmentation and Advanced ML Algorithms with Duck Pack Optimizer." International Research Journal of Multidisciplinary Technovation, March 24, 2025, 108–20. https://doi.org/10.54392/irjmt2529.

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Untreated glaucoma, a chronic eye illness, can cause irreversible vision loss if not caught early. The condition begins with abnormalities in the eye's drainage flow, leading to a rise in intraocular pressure. As the disease progresses, the optic nerve head deteriorates, resulting in vision loss. Ophthalmologists need extensive training and expertise to interpret findings accurately during medical follow-ups to examine the retina. To address this challenge, deep learning-based algorithms have been developed to screen for and diagnose glaucoma using images of the optic nerve, retinal structures

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Ashika, T., and G. Hannah Grace. "Enhancing heart disease prediction with stacked ensemble and MCDM-based ranking: an optimized RST-ML approach." Frontiers in Digital Health 7 (June 19, 2025). https://doi.org/10.3389/fdgth.2025.1609308.

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IntroductionCardiovascular disease (CVD) is a leading global cause of death, necessitating the development of accurate diagnostic models. This study presents an Optimized Rough Set Theory-Machine Learning (RST-ML) framework that integrates Multi-Criteria Decision-Making (MCDM) for effective heart disease (HD) prediction. By utilizing RST for feature selection, the framework minimizes dimensionality while retaining essential information.MethodsThe framework employs RST to select relevant features, followed by the integration of nine ML classifiers into five stacked ensemble models through corre

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Raihan, Md Johir, Md Al-Masrur Khan, Seong-Hoon Kee, and Abdullah-Al Nahid. "Detection of the chronic kidney disease using XGBoost classifier and explaining the influence of the attributes on the model using SHAP." Scientific Reports 13, no. 1 (2023). http://dx.doi.org/10.1038/s41598-023-33525-0.

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AbstractChronic kidney disease (CKD) is a condition distinguished by structural and functional changes to the kidney over time. Studies show that 10% of adults worldwide are affected by some kind of CKD, resulting in 1.2 million deaths. Recently, CKD has emerged as a leading cause of mortality worldwide, making it necessary to develop a Computer-Aided Diagnostic (CAD) system to diagnose CKD automatically. Machine Learning (ML) based CAD system can be used by a clinician to automatically diagnoses mass people. Since ML models are considered a black box, it is also necessary to expose influentia

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Brorsen, Lauritz F., James S. McKenzie, Fernanda E. Pinto, et al. "Metabolomic profiling and accurate diagnosis of basal cell carcinoma by MALDI imaging and machine learning." Experimental Dermatology 33, no. 7 (2024). http://dx.doi.org/10.1111/exd.15141.

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AbstractBasal cell carcinoma (BCC), the most common keratinocyte cancer, presents a substantial public health challenge due to its high prevalence. Traditional diagnostic methods, which rely on visual examination and histopathological analysis, do not include metabolomic data. This exploratory study aims to molecularly characterize BCC and diagnose tumour tissue by applying matrix‐assisted laser desorption ionization mass spectrometry imaging (MALDI‐MSI) and machine learning (ML). BCC tumour development was induced in a mouse model and tissue sections containing BCC (n = 12) were analysed. The

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Verma, Anurag, Po Ya Hsu, Colleen Kripke, WILLIAM HOWARD, Giorgio Sirugo, and Kelly Myes. "Abstract 4147767: Advanced Machine Learning Models for Classifying Transthyretin Amyloidosis in Clinical Settings." Circulation 150, Suppl_1 (2024). http://dx.doi.org/10.1161/circ.150.suppl_1.4147767.

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Introduction: Early and accurate classification of transthyretin amyloidosis (ATTR) is crucial for improving patient outcomes. However, nonspecific symptoms and heterogeneous disease variations have made ATTR diagnosis challenging. Leveraging advancements in machine learning (ML) and large language models (LLMs), this study aims to enhance diagnostic accuracy by analyzing electronic health records (EHRs) data. Hypothesis: Can the integration of innovative feature formulation and large language models improve the performance of ML models in diagnosing ATTR using EHR data? Goals/Aims: Our primar

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Deng, Kuo, Xiaomeng Ye, Kun Wang, Angelina Pennino, Abigail Jarvis, and Yola Hall. "Fully Interpretable and Adjustable Model for Depression Diagnosis: A Qualitative Approach." International FLAIRS Conference Proceedings 38 (May 14, 2025). https://doi.org/10.32473/flairs.38.1.138733.

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Recent advances in machine learning (ML) have enabled AI applications in mental disorder diagnosis, but many methods remain black-box or rely on post-hoc explanations which are not straightforward or actionable for mental health practitioners. Meanwhile, interpretable methods, such as k-nearest neighbors (k-NN) classification, struggle with complex or high-dimensional data. Moreover, there is a lack of study on users' real experience with interpretable AI. This study demonstrates a network-based k-NN model (NN-kNN) that combines the interpretability with the predictive power of neural networks

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Deng, Guobing, Jichong Zhu, Qing Lu, et al. "Application of machine learning in prediction of bone cement leakage during single-level thoracolumbar percutaneous vertebroplasty." BMC Surgery 23, no. 1 (2023). http://dx.doi.org/10.1186/s12893-023-01959-y.

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Abstract Background In the elderly, osteoporotic vertebral compression fractures (OVCFs) of the thoracolumbar vertebra are common, and percutaneous vertebroplasty (PVP) is a common surgical method after fracture. Machine learning (ML) was used in this study to assist clinicians in preventing bone cement leakage during PVP surgery. Methods The clinical data of 374 patients with thoracolumbar OVCFs who underwent single-level PVP at The First People's Hospital of Chenzhou were chosen. It included 150 patients with bone cement leakage and 224 patients without it. We screened the feature variables

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Harun, Dr Md Abu, Mohammad Mazumder, Dr Abu Shikder, Dr Nazneen Karim, and Dr Md Shahedur Hera. "Predictive Machine learning Models for necessity Supplemental Anesthesia in Endodontic treatment." Medical Research Archives 12, no. 4 (2024). http://dx.doi.org/10.18103/mra.v12i4.5296.

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Purpose: In cases of irreversible pulpitis, controlling intraoperative endodontic discomfort is extremely difficult, and patient satisfaction plays a big part in this. In order to forecast a diagnostic's and a treatment's outcome, machine learning (ML) has recently been implemented in the fields of medicine and dentistry. The goal of this work was to create machine learning (ML) models that could predict the need for further anesthesia. Methods: According to inclusion and exclusion criteria, this study included 128 individuals with endodontic discomfort. All patients underwent a clinical evalu

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Baker, Merryn J., Jeff Gordon, Aruvi Thiruvarudchelvan, Deborah H. Yates, and William Alexander Donald. "Rapid, Non-Invasive Breath Analysis for Enhancing Detection of Silicosis Using Mass Spectrometry and Interpretable Machine Learning." Journal of Breath Research, March 3, 2025. https://doi.org/10.1088/1752-7163/adbc11.

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Abstract Occupational lung diseases, such as silicosis, are a significant global health concern, especially with increasing exposure to engineered stone dust. Early detection of silicosis is helpful for preventing disease progression, but existing diagnostic methods, including X-rays, computed tomography scans, and spirometry, often detect the disease only at late stages. This study investigates a rapid, non-invasive diagnostic approach using atmospheric pressure chemical ionization-mass spectrometry (APCI-MS) to analyse volatile organic compounds (VOCs) in exhaled breath from 31 silicosis pat

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Williams, Kristin. "Evaluations of artificial intelligence and machine learning in neurodiagnostics." Journal of Neurophysiology, March 27, 2024. http://dx.doi.org/10.1152/jn.00404.2023.

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This paper evaluates the ethical implications of utilizing artificial intelligence (AI) algorithms in neurological diagnostic examinations. Applications of AI technology have been utilized to aid in the determination of pharmacological dosages of gadolinium for brain lesion detection, localization of seizure foci, and the characterization of large vessel occlusion in ischemic stroke patients. Multiple subtypes of AI- machine learning (AI/ML) algorithms are analyzed as AI-assisted neurology utilizes supervised, unsupervised, artificial neural network (ANN), and deep neural network (DNN) learnin

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Yıldız, Doğan, Gülcan Yıldız, and Sercan Demirci. "Makine Öğrenmesi Tekniklerinin Sürüş Stili Sınıflandırmasında Kullanımı." Black Sea Journal of Engineering and Science, July 10, 2024. http://dx.doi.org/10.34248/bsengineering.1457913.

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Sürücü davranışlarının trafik güvenliğine önemli derecede etkisi vardır. Bu nedenle, sürücülerin davranışsal örüntüleri ve bu örüntüleri etkileyen etmenler tanımlanmalıdır. Sürücüler, araçlarını daha verimli ve kurallara uygun kullanmaya yönlendirilmelidir. Bu bağlamda, sürücünün aracını nasıl kullandığı gözlemlenerek, sürücülerin sürüş risk derecelerine uygun olarak sigorta ya da kasko ücretleri belirlenebilir. Bu çalışmada, Araç İçi Teşhis (On Board Diagnostics- II, OBD-II) ve Küresel Konumlandırma Sistemi (Global Positioning System, GPS) cihazlarından alınan işlenmiş ve etiketlenmiş telemet

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Aghdam, Maryam Akhavan, Serdar Bozdag, and Fahad Saeed. "Alzheimer’s disease diagnosis using gray matter of T1‐weighted sMRI data and vision transformer." Alzheimer's & Dementia 20, S2 (2024). https://doi.org/10.1002/alz.089944.

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AbstractBackgroundAlzheimer's Disease (AD) is a progressive neurodegenerative disorder characterized by memory loss and cognitive decline. Traditional diagnostic methods, mainly based on cognitive, memory, and behavioral tests, have limitations, particularly in the early detection of AD. Structural magnetic resonance imaging (sMRI) has emerged as a key tool in understanding the brain changes associated with AD, focusing particularly on alterations in gray matter (GM). However, the complexity of brain changes in AD requires sophisticated analysis methods. In recent years, machine learning (ML)

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Alshenaifi, Reem, Yahya Alqahtani, Shabnam MA, and Snekhalatha Umapathy. "AI-Driven Framework for Automated Detection of Kidney Stones in CT Images: Integration of Deep Learning Architectures and Transformers." Biomedical Physics & Engineering Express, July 24, 2025. https://doi.org/10.1088/2057-1976/adf3ba.

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Abstract Abstract. Purpose: Kidney stones, a prevalent urological condition, associated with acute pain requires prompt and precise diagnosis for optimal therapeutic intervention. While computed tomography (CT) imaging remains the definitive diagnostic modality, manual interpretation of these images is a labor-intensive and error-prone process. This research endeavors to introduce Artificial Intelligence based methodology for automated detection and classification of renal calculi within the CT images. Method: To identify the CT images with kidney stones, a comprehensive exploration of various

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Pallapothu, Anika. "Diagnosis of Coronary Artery Disease using Adult Data from Blood Tests and Electrocardiograms." Journal of Student Research 12, no. 4 (2023). http://dx.doi.org/10.47611/jsrhs.v12i4.6245.

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Objective: Many modifiable risk factors affect the onset of coronary artery disease (CAD), a condition that is extremely common throughout the globe. Predictive models created using machine learning (ML) algorithms may help physicians identify CAD earlier and may lead to better results. The goal of this project was to use ML algorithms to predict CAD in patients.  Methods: The gathered dataset of UCI heart disease was used in this study to evaluate a variety of machine learning methods to predict CAD. Just the most crucial aspects of the hypothesis testing method were k

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Kirongo, Amos Chege, Geoffrey Muchiri Muketha, Eric Mworia, and Daniel Maitethia. "Application of Real-Time Deep Learning in integrated Surveillance of Maize and Tomato Pests and Bacterial Diseases." Journal of the Kenya National Commission for UNESCO, January 15, 2024. http://dx.doi.org/10.62049/jkncu.v4i1.46.

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With an emphasis on maize and tomato crops specifically, this research explores the creative fusion of computer vision (CV) and machine learning (ML) to address the enduring problem of pests and crop diseases impacting Kenya's crucial agricultural industry. This study aims to provide farmers with a reliable and accurate tool for identifying pests and diagnosing diseases by using a MobileNetV2-based model. An extensive dataset including photos of both healthy and sick crops was gathered, and preprocessing approaches, such as data augmentation, were used to improve the model's training procedure

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Li, Yatian, Quan Chen, Jingnan Wu, et al. "Machine Learning‐Driven Speech Biomarker Analysis: A Novel Approach for Detecting Cognitive Decline in Older Adults." Alzheimer's & Dementia 20, S2 (2024). https://doi.org/10.1002/alz.089668.

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AbstractBackgroundSpeech impairment appears at early stages of Alzheimer’s disease. A mobile voice recognition‐based cognitive assessment tool, Shanghai Cognitive Screening (SCS), was developed for detecting mild cognitive impairment (MCI) and dementia in the community. The objective of this study is to investigate speech biomarkers associated with cognitive impairments based on SCS, and to evaluate the diagnostic accuracy of speech feature‐based machine learning (ML) models for detecting MCI.MethodA total of 301 older adults were recruited to perform SCS assessments, with 135 of them were dia

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Karim, Hamidi Machekposhti, Sedghi Hossein, Telvari Abdolrasoul, and Babazadeh Hossein. "Flood Predicting in Karkheh River Basin Using Stochastic ARIMA Model." International Journal of Biological, Life and Agricultural Sciences 11.0, no. 3 (2018). https://doi.org/10.5281/zenodo.1316109.

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Floods have huge environmental and economic impact. Therefore, flood prediction is given a lot of attention due to its importance. This study analysed the annual maximum streamflow (discharge) (AMS or AMD) of Karkheh River in Karkheh River Basin for flood predicting using ARIMA model. For this purpose, we use the Box-Jenkins approach, which contains four-stage method model identification, parameter estimation, diagnostic checking and forecasting (predicting). The main tool used in ARIMA modelling was the SAS and SPSS software. Model identification was done by visual inspection on the ACF and P

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Dai, Hao, Yu Huang, Xing He, et al. "Optimizing Strategy for Lung Cancer Screening: From Risk Prediction to Clinical Decision Support." JCO Clinical Cancer Informatics, no. 9 (May 2025). https://doi.org/10.1200/cci-24-00291.

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PURPOSE Low-dose computed tomography (LDCT) screening is effective in reducing lung cancer mortality by detecting the disease at earlier, more treatable stages. However, high false-positive rates and the associated risks of subsequent invasive diagnostic procedures present significant challenges. This study proposes an advanced pipeline that integrates machine learning (ML) and causal inference techniques to optimize lung cancer screening decisions. MATERIALS AND METHODS Using real-world data from the OneFlorida+ Clinical Research Consortium, we developed ML models to predict individual lung c

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Brar, Ramneek Kaur, and Manoj Sharma. "ET‐WOFS Metaheuristic Feature Selection Based Approach for Endometrial Cancer Classification and Detection." International Journal of Imaging Systems and Technology 35, no. 4 (2025). https://doi.org/10.1002/ima.70126.

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ABSTRACTEndometrial Cancer (EC), also referred to as endometrial carcinoma, stands as the most common category of carcinoma of the uterus in females, ranking as the sixth most common cancer worldwide among women. This study introduces a Machine Learning‐Based Efficient Computer‐Aided Diagnosis (ML‐CAD) state‐of‐the‐art model aimed at assisting healthcare professionals in investigating, estimating, and accurately classifying endometrial cancer through the meticulous analysis of H&E‐stained histopathological images. In the initial phase of image processing, meticulous steps are taken to elim

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Mahey, Sahil, and Hamid Usefi. "Incorporating Wave-ViT for Breast Cancer Diagnosis Using MRI Imaging." International FLAIRS Conference Proceedings 38 (May 14, 2025). https://doi.org/10.32473/flairs.38.1.138756.

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Breast cancer remains one of the leading causes of mortality among women globally, and early detection is critical for improving survival rates. Breast MRI, the most sensitive imaging modality for detection, often involves manual review of numerous slices, which is time-intensive and prone to human error. Machine learning (ML) algorithms offer a transformative solution by automating this process, improving efficiency, and enhancing diagnostic accuracy. In this study, we propose a machine learning approach to enhance breast cancer prediction and diagnosis. We utilize a pre-trained multiscale vi

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