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Journal articles on the topic 'Cardiovascular disease Confusion Matrix'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

esh, Ram, Rathidevi R, and Priya nandhini. "Prediction of Cardiovascular Disease using NaïveBayes with Confusion Matrix." International Journal of Computer Science and Engineering 10, no. 12 (2023): 5–9. http://dx.doi.org/10.14445/23488387/ijcse-v10i12p102.

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Shiva, Shrivastava*1 &. Neeraj Mehta2. "DIAGNOSIS OF HEART DISEASE USING GENETICALLY OPTIMIZED NEURAL NETWORK." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 7 (2017): 142–49. https://doi.org/10.5281/zenodo.823060.

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Cardiovascular diseases are a major public health problem and are expected to continue to be in the future due mainly to population aging; In Spain, represent the leading cause of death and hospitalization. The three most important cardiovascular problems, ischemic heart disease, cerebrovascular disease and heart failure are based on the large epidemiological studies.This paper proposes Genetically optimized Neural Network technique for heart disease prediction. Performance of proposed approach is evaluated using confusion matrix plot.

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Kandwal, Kuldeep. "AI-Based Prediction of Cardiovascular Disease." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem46154.

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Abstract: Cardiovascular diseases (CVD) are still one of the leading causes of death in the world today which shows that early diagnosis and preventive measures are vital for improving the outcomes post death. The purpose of this project is to determine the potential risk of a patient suffering from cardiovascular disease through the application of artificial intelligence (AI), deep learning (DL) and machine learning (ML). The health parameters include age, gender, blood pressure, cholesterol levels, heart rate, exercise tolerance and the dataset also has a target variable characteristic respo

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Noori Mohammad Ali, Sara, and Nawzad Muhammed Ahmed. "Comparing some Machine Learning Models for Cardiovascular Disease." Journal of Pioneering Medical Sciences 14, no. 04 (2025): 60–66. https://doi.org/10.47310/jpms2025140408.

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Background and Aim: Cardiovascular disease remains a leading cause of morbidity and mortality worldwide, necessitating the development of accurate predictive models for early diagnosis. Therefore, this study aimed to evaluate and compare the performance of three machine learning models-Random Forest, Decision Tree, and K-Nearest Neighbors-in predicting cardiovascular disease based on key risk factors. Method: This retrospective study utilized patient data from Shar Hospital in Sulaimaniyah City. The dataset included demographic and clinical risk factors such as age, smoking status, diabetes, h

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Tsehay, Admassu Assegie, Kumar Napa Komal, Thulasi Thiyagu, Kalyan Kumar Angati, Jeyanthiran Thiruvarasu Vasantha Priya Maran, and Dhamodaran Vigneswari. "Scalability and performance of decision tree for cardiovascular disease prediction." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 3 (2024): 2540–45. https://doi.org/10.11591/ijai.v13.i3.pp2540-2545.

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As one of the most common types of disease, cardiovascular disease is a serious health concern worldwide. Early detection is crucial for successful treatment and improved survival rates. The decision tree is a robust classifier for predicting the risk of cardiovascular disease and getting insights that would assist in making clinical decisions. However, selecting a better model for cardiovascular disease could be challenging due to scalability issues. Hence, this study examines the scalability and performance of decision trees for cardiovascular disease prediction. The study evaluated the perf

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Admassu Assegie, Tsehay, Komal Kumar Napa, Thiyagu Thulasi, Angati Kalyan Kumar, Maran Jeyanthiran Thiruvarasu Vasantha Priya, and Vigneswari Dhamodaran. "Scalability and performance of decision tree for cardiovascular disease prediction." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 3 (2024): 2540. http://dx.doi.org/10.11591/ijai.v13.i3.pp2540-2545.

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<span lang="EN-US">As one of the most common types of disease, cardiovascular disease is a serious health concern worldwide. Early detection is crucial for successful treatment and improved survival rates. The decision tree is a robust classifier for predicting the risk of cardiovascular disease and getting insights that would assist in making clinical decisions. However, selecting a better model for cardiovascular disease could be challenging due to scalability issues. Hence, this study examines the scalability and performance of decision trees for cardiovascular disease prediction. The

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El Massari, Hakim, Noreddine Gherabi, Sajida Mhammedi, Hamza Ghandi, Mohamed Bahaj, and Muhammad Raza Naqvi. "The Impact of Ontology on the Prediction of Cardiovascular Disease Compared to Machine Learning Algorithms." International Journal of Online and Biomedical Engineering (iJOE) 18, no. 11 (2022): 143–57. http://dx.doi.org/10.3991/ijoe.v18i11.32647.

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Cardiovascular disease is one of the chronic diseases that is on the rise. The complications occur when cardiovascular disease is not discovered early and correctly diagnosed at the right time. Various machine learning approaches, including ontology-based Machine Learning techniques, have lately played an essential role in medical science by building an automated system that can identify heart illness. This paper compares and reviews the most prominent machine learning algorithms, as well as ontology-based Machine Learning classification. Random Forest, Logistic regression, Decision Tree, Naiv

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Liu, Hongji, Yadong Tian, and Donghong Yu. "Prediction of cardiovascular and cerebrovascular diseases based on machine learning models." Applied and Computational Engineering 46, no. 1 (2024): 35–44. http://dx.doi.org/10.54254/2755-2721/46/20241068.

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Recently we had the fact that cardiovascular disease has become one of the major threats to human life, which leads to the significance of the research around the prevention and cure of such disease. Recently, machine learning algorithms are utilized for the prediction of a certain person who has an illness or not. To verify the effectiveness of predicting cardiovascular disease using machine learning methods, we predict cardiovascular disease given features of a persons life habits and illness history from the Behavioral Risk Factor Surveillance System. Therefore, 5 models are selected, inclu

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Li, Steven Kalung, and Yuli Peng. "Cardiovascular Diseases Risk Prediction Based on Machine Learning Models." Highlights in Science, Engineering and Technology 81 (January 26, 2024): 473–77. http://dx.doi.org/10.54097/wdsr1890.

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Cardiovascular Diseases (CVD) are the leading cause of death worldwide, CVD has a high death rate of 95% as it causes a blockage in blood vessels, which prevents blood from flowing to the brain or heart. Necessitating accurate risk prediction for effective prevention and management becomes a necessity of lowering death caused by cardiovascular disease. This paper presents a comprehensive study utilizing Logistic Regression and Decision Tree models implemented via Python to predict CVD risk. The models were trained on a dataset comprising numerous variables such as age, checkup, sex, and BMI wh

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Jing, Shuyue. "Comparison of dimensionality reduction methods and evaluation of prediction effect based on cardiovascular disease data." Theoretical and Natural Science 27, no. 1 (2023): 190–201. http://dx.doi.org/10.54254/2753-8818/27/20240731.

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Cardiovascular diseases have become the leading cause of death in the world, and the mortality rate caused by them is still on the rise, which is a major challenge facing the world. In the study of cardiovascular diseases, there are many kinds and quantities of factors leading to the disease, so how to screen more effective factors for research and accurate prediction is an important problem. The aim of this study is to conduct an in-depth comparative analysis of multiple dimensionality reduction methods for cardiovascular disease data. This paper evaluates the results produced by various dime

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Prusty, Sashikanta, Srikanta Patnaik, and Sujit Kumar Dash. "Comparative analysis and prediction of coronary heart disease." Indonesian Journal of Electrical Engineering and Computer Science 27, no. 2 (2022): 944. http://dx.doi.org/10.11591/ijeecs.v27.i2.pp944-953.

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Cardiovascular disease (CVD) <span>is now one of the leading causes of death worldwide and was also thought to be a serious illness in the mid and old ages. Artificial intelligence and machine learning have a huge impact on the healthcare areas. As a result, getting a familiar individual with data processing techniques suitable for numerical health data. Although, the most often used algorithms for classification tasks will be incredibly advantageous in terms of time management. In particular here, a common procedure has been proposed for predicting cardiovascular disease. Accordingly, w

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Prusty, Sashikanta, Srikanta Patnaik, and Sujit Kumar Dash. "Comparative analysis and prediction of coronary heart disease." Indonesian Journal of Electrical Engineering and Computer Science 27, no. 2 (2022): 944–53. https://doi.org/10.11591/ijeecs.v27.i2.pp944-953.

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Cardiovascular disease (CVD) is now one of the leading causes of death worldwide and was also thought to be a serious illness in the mid and old ages. Artificial intelligence and machine learning have a huge impact on the healthcare areas. As a result, getting a familiar individual with data processing techniques suitable for numerical health data. Although, the most often used algorithms for classification tasks will be incredibly advantageous in terms of time management. In particular here, a common procedure has been proposed for predicting cardiovascular disease. Accordingly, we herein con

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Abdelhafeez, Ahmed, Abdullah Ashraf, and Hussam Elbehiery. "Harnessing Machine Learning for Accurate Cardiovascular Disease Prediction." SciNexuses 1 (January 29, 2024): 9–15. http://dx.doi.org/10.61356/j.scin.2024.1267.

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Cardiovascular disease (CVD) is a life-threatening disease rising considerably in the world. Early detection and prediction of CVD as well as other heart diseases might protect many lives. This requires tact clinical data analysis. The potential of predictive machine learning algorithms to develop the doctor’s perception is essential to all stakeholders in the health sector since it can augment the efforts of doctors to have a healthier climate for patient diagnosis and treatment. We used the machine learning (ML) algorithm to carry out a significant explanation for accurate prediction and dec

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Jin, Jiayi, and Chengyun Zhao. "Performance Analysis and Comparison of Heart Disease Prediction Models." Highlights in Science, Engineering and Technology 123 (December 24, 2024): 618–24. https://doi.org/10.54097/yb7t2031.

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Since cardiovascular diseases (CVDs) are the leading cause of death, it is significant for people to detect them early and take certain precautions. As a result, the paper performs the study of heart disease prediction with statistical models. In this study, the researcher analyzed a dataset from four different regions, including Cleveland, Hungarian, Switzerland, and Long Beach. Each of the regions is used as one testing dataset and the rest of the three regions are used as one training dataset, with a total of four sets of training and testing datasets. The goal is to predict heart disease w

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Avanzato, Roberta, and Francesco Beritelli. "Automatic ECG Diagnosis Using Convolutional Neural Network." Electronics 9, no. 6 (2020): 951. http://dx.doi.org/10.3390/electronics9060951.

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Cardiovascular disease (CVD) is the most common class of chronic and life-threatening diseases and, therefore, considered to be one of the main causes of mortality. The proposed new neural architecture based on the recent popularity of convolutional neural networks (CNN) was a solution for the development of automatic heart disease diagnosis systems using electrocardiogram (ECG) signals. More specifically, ECG signals were passed directly to a properly trained CNN network. The database consisted of more than 4000 ECG signal instances extracted from outpatient ECG examinations obtained from 47

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Adi Nugroho, Agustinus Bimo Gumelar, Adri Gabriel Sooai, Dyana Sarvasti, and Paul L Tahalele. "Perbandingan Performansi Kinerja Algoritma Pengklasifikasian Terpandu Untuk Kasus Penyakit Kardiovaskular." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 4, no. 5 (2020): 998–1006. http://dx.doi.org/10.29207/resti.v4i5.2316.

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One of the health problems that occur in Indonesia is the increasing number of NCD (Non-Communicable Disease) such as heart attack and cardiovascular disease. There are two factors that cause cardiovascular disease, i.e. factor that can be changed and cannot be changed. This study aim to analyze the best performance of several classification algorithms such as k-nearest neighbors algorithm (k-NN), stochastic gradient descent (SGD), random forest (RF), neural network (NN) and logistic regression (LR) in classifying cardiovascular based on factors that caused those diseases. There are two aspect

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Mudzramer A. Hayudini, Datu Ansaruddin K. Kiram, Mharcelyn M. Kiram, Abdulkamal H. Abduljalil, Nureeza J. Latorre, and Fahra B. Sahibad. "Predictive Modeling in Cardiovascular Disease: An Investigation of Random Forests." Natural Sciences Engineering and Technology Journal 5, no. 1 (2024): 393–404. https://doi.org/10.37275/nasetjournal.v5i1.60.

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Cardiovascular diseases (CVDs) are a leading cause of death worldwide. Early detection and intervention are crucial for improving patient outcomes. Machine learning (ML) offers promising tools for CVD prediction, with random forests (RF) emerging as a robust and versatile algorithm. This study investigates the application of RF in predicting blood pressure categories, a crucial indicator of cardiovascular health, using a comprehensive dataset of patient metrics. This study investigated the application of RF in predicting blood pressure categories, a crucial indicator of cardiovascular health.

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Khan, Arsalan, Moiz Qureshi, Muhammad Daniyal, and Kassim Tawiah. "A Novel Study on Machine Learning Algorithm-Based Cardiovascular Disease Prediction." Health & Social Care in the Community 2023 (February 20, 2023): 1–10. http://dx.doi.org/10.1155/2023/1406060.

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Cardiovascular disease (CVD) is a life-threatening disease rising considerably in the world. Early detection and prediction of CVD as well as other heart diseases might protect many lives. This requires tact clinical data analysis. The potential of predictive machine learning algorithms to develop the doctor’s perception is essential to all stakeholders in the health sector since it can augment the efforts of doctors to have a healthier climate for patient diagnosis and treatment. We used the machine learning (ML) algorithm to carry out a significant explanation for accurate prediction and dec

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Utami, Nisa, Kiki Ahmad Baihaqi, Elsa Elvira Awal, and Deden Waiddin. "Analisis Kinerja Algoritma Decision Tree Dan Random Forest Dalam Klasifikasi Penyakit Kardiovaskular." Building of Informatics, Technology and Science (BITS) 6, no. 2 (2024): 970–80. https://doi.org/10.47065/bits.v6i2.5722.

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Cardiovascular disease is a disease with a fairly high number of deaths. In Indonesia, the term cardiovascular is more popular with heart disease, which is a condition that can cause narrowing and blockage of blood vessels. Cardiovascular disease has two risks, the first is a risk that can be changed, such as stress, increased blood pressure, unhealthy diet, increased glucose levels, abnormal cholesterol and lack of physical activity. Meanwhile, risks that cannot be changed include family disease, gender, age and obesity. In this research, we can examine and analyze the performance of the two

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Korial, Ayad E., Ivan Isho Gorial, and Amjad J. Humaidi. "An Improved Ensemble-Based Cardiovascular Disease Detection System with Chi-Square Feature Selection." Computers 13, no. 6 (2024): 126. http://dx.doi.org/10.3390/computers13060126.

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Cardiovascular disease (CVD) is a leading cause of death globally; therefore, early detection of CVD is crucial. Many intelligent technologies, including deep learning and machine learning (ML), are being integrated into healthcare systems for disease prediction. This paper uses a voting ensemble ML with chi-square feature selection to detect CVD early. Our approach involved applying multiple ML classifiers, including naïve Bayes, random forest, logistic regression (LR), and k-nearest neighbor. These classifiers were evaluated through metrics including accuracy, specificity, sensitivity, F1-sc

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Nugroho, Nur Cahyo Tio, and Erwin Yudi Hidayat. "Implementation of Adam Optimizer using Recurrent Neural Network (RNN) Architecture for Diabetes Classification." JURNAL MEDIA INFORMATIKA BUDIDARMA 8, no. 1 (2024): 421. http://dx.doi.org/10.30865/mib.v8i1.7254.

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Non-communicable diseases (NCDs) present a considerable worldwide health dilemma, resulting in considerable expenses for treatment and heightened rates of mortality. Conditions like diabetes mellitus, cardiovascular diseases, cancer, and chronic respiratory diseases are primary causes of global mortality, making up 71% of total global deaths in 2016, as reported by the World Health Organization (WHO). Diabetes Mellitus (DM), marked by prolonged elevated blood glucose levels, stands out as a significant metabolic disorder. This research delves into the implementation of Recurrent Neural Network

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Arif, Siti Novianti Nuraini, Amril Mutoi Siregar, Sutan Faisal, and Ayu Ratna Juwita. "Klasifikasi Penyakit Serangan Jantung Menggunakan Metode Machine Learning K-Nearest Neighbors (KNN) dan Support Vector Machine (SVM)." JURNAL MEDIA INFORMATIKA BUDIDARMA 8, no. 3 (2024): 1617. http://dx.doi.org/10.30865/mib.v8i3.7844.

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Cardiovascular disease (CVD) is a general term for disorders related to the heart, coronary arteries, and blood vessels. These diseases are most commonly caused by blocked blood vessels, either due to fat buildup or internal bleeding. According to the WHO, each year, cardiovascular diseases account for 32% of all deaths, which translates to about 17.9 million people annually. The numerous factors causing CVD make it challenging for doctors to diagnose patients who are at low or higher risk of heart attacks. A machine learning model is needed for the early recognition of heart attack symptoms.

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Nadheer, Israa. "Heart Disease Prediction System using hybrid model of Multi-layer perception and XGBoost algorithms." BIO Web of Conferences 97 (2024): 00047. http://dx.doi.org/10.1051/bioconf/20249700047.

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Multi-layer perceptron (MLP) algorithms play a critical role in improving the accuracy and effectiveness of heart disease diagnosis in the context of the machine learning research. This paper presents an approach of heart disease prediction involves RReliefF-based feature importance assessment then MLP-based classification of features into three groups based on importance scores is proposed. The study employs three feedforward neural networks to classify effectively the clustered groups. Furthermore, an integrated approach utilizes XGBoost ensemble classification, leveraging boosted ensemble l

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MOHSIN, AHMAD, and OLIVER FAUST. "AUTOMATED CHARACTERIZATION OF CARDIOVASCULAR DISEASES USING WAVELET TRANSFORM FEATURES EXTRACTED FROM ECG SIGNALS." Journal of Mechanics in Medicine and Biology 19, no. 01 (2019): 1940009. http://dx.doi.org/10.1142/s0219519419400098.

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Cardiovascular disease has been the leading cause of death worldwide. Electrocardiogram (ECG)-based heart disease diagnosis is simple, fast, cost effective and non-invasive. However, interpreting ECG waveforms can be taxing for a clinician who has to deal with hundreds of patients during a day. We propose computing machinery to reduce the workload of clinicians and to streamline the clinical work processes. Replacing human labor with machine work can lead to cost savings. Furthermore, it is possible to improve the diagnosis quality by reducing inter- and intra-observer variability. To support

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Anandha Praba, R., L. Suganthi, E. S. Selva Priya, and J. Jeslin Libisha. "Efficient Cardiac Arrhythmia Detection Using Machine Learning Algorithms." Journal of Physics: Conference Series 2318, no. 1 (2022): 012011. http://dx.doi.org/10.1088/1742-6596/2318/1/012011.

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Abstract The most common type of chronic and life-threatening disease is cardiovascular disease (CVD). For the early prediction of arrhythmia, electrocardiogram (ECG) is recorded from the patients, non-invasively using surface electrode. In this approach, Empirical Mode Decomposition (EMD) is performed for noise removal followed by Pan Tompkins algorithm for feature extraction. To reduce the amount of signal characteristics and computation time, Principal Component Analysis (PCA) is utilized. Finally, two classifiers, The Support Vector Machine (SVM) and the Naive Bayes (NB) classifier is used

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Jakin, KABONGO, LUZOLO CLEM'S MPOVO, MANIAMFU PAVODI, and KAMANDA LOUISON DUMBI. "Five Machine Learning Supervised Algorithms for The Analysis and the Prediction of Obesity." International Journal of Innovative Science and Research Technology 7, no. 1 (2023): 1956–64. https://doi.org/10.5281/zenodo.7551285.

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Obesity and overweight are major risk factors for a variety of chronic diseases, including cardiovascular diseases like heart disease and stroke, which are the main leading causes of most deaths worldwide. Obesity can also lead to diabetes and its complications, such as blindness, limb amputations, and the need for dialysis. Diabetes prevalence has quadrupled worldwide since 1980. Excess weight can also cause musculoskeletal disorders such as osteoarthritis. The objective of this research is to analyze and predict obesity using machine learning algorithms to assist clinicians and public health

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Edwin Elisha Omondi. "Heart disease prediction model using random forest classifier." World Journal of Advanced Research and Reviews 26, no. 2 (2025): 3468–90. https://doi.org/10.30574/wjarr.2025.26.2.3447.

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To forecast the risk of heart disease, I have created a random forest classifier model in this work. I trained the algorithm to identify people into two groups: those at low risk (0) and those at high risk (1) of acquiring heart disease. I did this by utilizing a dataset made up of anonymized patient information. The model's remarkable 88.04% accuracy rate shows how well it can differentiate between the two classes. By thoroughly analyzing the model's performance, I produced an extensive classification report that included information on accuracy overall, precision, recall, and F1-score for ev

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Deshpande, Dr Bhagwant K. "Classifying And Predicting Adolescent Cardiac Health Using KNN." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem46191.

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Abstract- This study uses a K-Nearest Neighbours (KNN) classifier to detect teenage cardiac disease. Years of age, Sexual orientation, Chest Ache category, Ambient Heart pressure, cholesterol levels, Highest Cardiac Level, and Exercise-Induced Angina are clinically important and interpretable aspects of the cardiovascular disease dataset used to create the model. These qualities were chosen to improve model interpretability for doctors and laypeople. Data preparation encoded categorical variables and standardised features for model optimisation. Histograms, charts with bars, and scatter plots

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Nurriski, Yopi Julia, and Alamsyah Alamsyah. "Optimasi Deep Convolutional Neural Network (Deep CNN) untuk Deteksi Aritmia Melalui Sinyal EKG Menggunakan Arsitektur Conv1D." Indonesian Journal of Mathematics and Natural Sciences 46, no. 1 (2023): 10–20. http://dx.doi.org/10.15294/ijmns.v46i1.46176.

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Cardiovascular disease (CVD) adalah penyebab signifikan morbiditas dan mortalitas di seluruh dunia. Aritmia menjadi salah satu kondisi yang paling parah dari CVD. Penyakit ini mengacu pada ketidakteraturan denyut atau irama jantung. Penyakit aritmia dapat diidentifikasi melalui rekaman sinyal elektrokardiogram (EKG). Pada penelitian ini, dilakukan data cleaning yang bertujuan untuk menghilangkan missing value pada dataset. Adapun metode yang digunakan untuk klasifikasi adalah Deep Convolutional Neural Network (Deep CNN) dengan arsitektur Conv1D. Oleh karena itu, dilakukan perubahan dimensi inp

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Umoren, Imeh, Oluwaseyi Abe, Godwin Ansa, Saviour Inyang, and Idongesit Umoh. "A New Index for Intelligent Classification of Early Syndromic of Cardiovascular (CVD) Diseases Based on Electrocardiogram (ECG)." European Journal of Computer Science and Information Technology 11, no. 4 (2023): 1–21. http://dx.doi.org/10.37745/ejcsit.2013/vol11n4121.

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Most disease that affects the heart or blood vessels is referred to as cardiovascular disease(CVD). The main aim of this work is to build a system capable of modeling and predicting early syndromic cardiovascular diseases (CVD) based on electrocardiogram (ECG). The study considers the implementation of computationally intelligent system for detecting and classifying early syndromic assessment of CVD. The clinical and ECG recordings of patients diagnosed with pulmonary hypertension at the University of Uyo Teaching Hospital (UUTH) were obtained. The datasets were segmented into Demographic and

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D, Cenitta, Arul N, Praveen Pai T, VIJAYA ARJUNAN RANGANATHAN, Tanuja Shailesh, and Andrew J. "An Explainable Transfer Learning based Residual Attention BiLSTM Model for Fair and Accurate Prognosis of Ischemic Heart Disease." F1000Research 14 (July 4, 2025): 651. https://doi.org/10.12688/f1000research.166307.1.

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Background Early and accurate prediction of Ischemic Heart Disease (IHD) is critical to reducing cardiovascular mortality through timely intervention. While deep learning (DL) models have shown promise in disease prediction, many lack interpretability, generalizability, and fairness—particularly when deployed across demographically diverse populations. These shortcomings limit clinical adoption and risk reinforcing healthcare disparities. Methods This study proposes a novel model: X-TLRABiLSTM (Explainable Transfer Learning–based Residual Attention Bidirectional LSTM). The architecture integra

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Lazutkina, Anna Yu. "Predictors of microalbuminuria in workers of locomotive crews: prospective observational study." CardioSomatics 14, no. 1 (2023): 27–36. http://dx.doi.org/10.17816/cs321275.

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BACKGROUND: Cardiorenal relationships are one of the key problems in cardiology and nephrology. Microalbuminuria is a symptom of kidney pathologies and cardiovascular diseases. Studying the causes of microalbuminuria will help in solving the issue of pathological cardiorenal relationships. AIM: To study the causes of the origin of microalbuminuria on the group of locomotive crews employees of the Trans-Baikal Railway. MATERIALS AND METHODS: Predictors of microalbuminuria were established using data from a 6-year prospective follow-up of 22 clinical and anamnestic items in a natural

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Rosdiana, Rosdiana, Vera Novalia, Ilham Saputra, Mutammimul Ula, and Muhammad Danil. "Application of Artificial Intelligence Chi-Square Model and Classification Of KNN in Heart Disease Detection." JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING 6, no. 1 (2022): 180–88. http://dx.doi.org/10.31289/jite.v6i1.7343.

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Cardiovascular disease is a problem in the blood vessels that do not run smoothly into the heart. This is fatal in patients with a history of heart disease. This problem often occurs in the flow of blood pumps into the heart. The problem examined in this study is how to complete the level of accuracy of each data set and the reduction of each attribute in heart disease. The purpose of this study is to analyze heart disease and classify heart disease using the chi-square and K-Nearest Neighbor algorithms. The results of the study with patient age 57, gender LK, cp 3, trestbps 200, chol 564, fbs

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Pratama, Luthfi Ilham Agus, and Anggyi Trisnawan Putra. "Optimizing Heart Disease Classification Using the Support Vector Machine Algorithm with Hybrid Particle Swarm and Grey Wolf Optimization." Recursive Journal of Informatics 3, no. 1 (2025): 26–33. https://doi.org/10.15294/rji.v3i1.737.

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Abstract. Heart disease, also known as cardiovascular disease, is a condition that affects the heart and blood vessels, leading to complications such as coronary artery disease, heart failure, arrhythmias, and heart valve disorders. According to the World Health Organization (WHO), approximately 17.9 million people die from heart disease each year. Early detection plays a crucial role in reducing the number of cases and improving patient outcomes.Purpose: In the era of rapid technological advancements, machine learning has been widely utilized for early diagnosis of heart disease. This study a

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Febriani, Vera, Dian Lestari, Sri Mardiyati, and Oktavia Lilyasari. "FUZZY LOGISTIC REGRESSION APPLICATION ON PREDICTIONS CORONARY HEART DISEASE." BAREKENG: Jurnal Ilmu Matematika dan Terapan 17, no. 1 (2023): 0571–80. http://dx.doi.org/10.30598/barekengvol17iss1pp0571-0580.

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According to the World Health Organization (WHO) in 2015, 70% of cardiac deaths were caused by coronary heart disease (CHD). Based on WHO data in 2017, 17.5 million deaths were recorded, equivalent to 30% of the total deaths in the world caused by coronary heart disease. Coronary heart disease is a disorder of heart function caused by plaque that accumulates in arterial blood vessels so that it interferes with the supply of oxygen to the heart tissue. This causes reduced blood flow to the heart muscle and oxygen deficiency occurs. In more serious circumstances, it can result in a heart attack.

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Miranda, Eka, Suko Adiarto, Faqir M. Bhatti, Alfi Yusrotis Zakiyyah, Mediana Aryuni, and Charles Bernando. "Understanding Arteriosclerotic Heart Disease Patients Using Electronic Health Records: A Machine Learning and Shapley Additive exPlanations Approach." Healthcare Informatics Research 29, no. 3 (2023): 228–38. http://dx.doi.org/10.4258/hir.2023.29.3.228.

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Objectives: The number of deaths from cardiovascular disease is projected to reach 23.3 million by 2030. As a contribution to preventing this phenomenon, this paper proposed a machine learning (ML) model to predict patients with arteriosclerotic heart disease (AHD). We also interpreted the prediction model results based on the ML approach and deployed modelagnostic ML methods to identify informative features and their interpretations.Methods: We used a hematology Electronic Health Record (EHR) with information on erythrocytes, hematocrit, hemoglobin, mean corpuscular hemoglobin, mean corpuscul

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Suriya, S., and N. H. Madhumitha. "Heart Failure Prediction using Gaussian Naïve Bayes Algorithm." June 2023 5, no. 2 (2023): 125–43. http://dx.doi.org/10.36548/jitdw.2023.2.004.

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Heart failure affects a minimum of 26 million individuals and its occurrence has been increasing day-by-day. Heart failure occurs when the heart cannot pump enough blood to meet the body's needs. This is caused due to various reasons such as coronary heart disease, heart valve malfunctioning, diabetes, anaemia etc. So, it is important to predict heart failure in its early stage to reduce the mortality rate. Cardiovascular disease is the major contributing factor for the prediction of heart failure. This research uses Gaussian Naïve Bayes technique which comes under supervised learning algorith

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Saurabh Pal, Ritu Aggrawal,. "Elimination and Backward Selection of Features (P-Value Technique) In Prediction of Heart Disease by Using Machine Learning Algorithms." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 6 (2021): 2650–65. http://dx.doi.org/10.17762/turcomat.v12i6.5765.

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Background: Early speculation of cardiovascular disease can help determine the lifestyle change options of high-risk patients, thereby reducing difficulties. We propose a coronary heart disease data set analysis technique to predict people’s risk of danger based on people’s clinically determined history. The methods introduced may be integrated into multiple uses, such for developing decision support system, developing a risk management network, and help for experts and clinical staff. Methods: We employed the Framingham Heart study dataset, which is publicly available Kaggle, to train s

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Lazutkina, A. Yu. "Formation and progression of microalbuminuria." "Arterial’naya Gipertenziya" ("Arterial Hypertension") 30, no. 6 (2024): 562–76. https://doi.org/10.18705/1607-419x-2024-2418.

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Relevance. Microalbuminuria (MAU) is a symptom diagnosed in kidney pathology, cardiovascular and other diseases. The study of the processes of development and progression of MAU will bring closer the solution of problems of nephrology, cardiology and pathological cardiorenal relationships.Objective. To study the development t and progression of MAU.Design and methods. Using the data from 6‑year follow-up of 22 clinical and anamnestic indicators of initially healthy 7,959 men (workers of locomotive crews) 18–66 years old, found out the origin of MAU and the progression of this pathological symp

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Wang, Jinwan, Shuai Wang, Mark Xuefang Zhu, Tao Yang, Qingfeng Yin, and Ya Hou. "Risk Prediction of Major Adverse Cardiovascular Events Occurrence Within 6 Months After Coronary Revascularization: Machine Learning Study." JMIR Medical Informatics 10, no. 4 (2022): e33395. http://dx.doi.org/10.2196/33395.

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Background As a major health hazard, the incidence of coronary heart disease has been increasing year by year. Although coronary revascularization, mainly percutaneous coronary intervention, has played an important role in the treatment of coronary heart disease, major adverse cardiovascular events (MACE) such as recurrent or persistent angina pectoris after coronary revascularization remain a very difficult problem in clinical practice. Objective Given the high probability of MACE after coronary revascularization, the aim of this study was to develop and validate a predictive model for MACE o

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Zhang, Tiexu, Shengming Huang, Pengfei Xie, et al. "Development of Machine Learning Tools for Predicting Coronary Artery Disease in the Chinese Population." Disease Markers 2022 (November 17, 2022): 1–18. http://dx.doi.org/10.1155/2022/6030254.

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Purpose. Coronary artery disease (CAD) is one of the major cardiovascular diseases and the leading cause of death globally. Blood lipid profile is associated with CAD early risk. Therefore, we aim to establish machine learning models utilizing blood lipid profile to predict CAD risk. Methods. In this study, 193 non-CAD controls and 2001 newly-diagnosed CAD patients (1647 CAD patients who received lipid-lowering therapy and 354 who did not) were recruited. Clinical data and the result of routine blood lipids tests were collected. Moreover, low-density lipoprotein cholesterol (LDL-C) subfraction

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Lazutkina, A. Yu. "Origin of stage I-II retinopathy." Aspirantskiy Vestnik Povolzhiya 23, no. 4 (2023): 38–43. http://dx.doi.org/10.55531/2072-2354.2023.23.4.38-43.

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Aim – to study the causes of retinopathy (RP) formation and progression at the initial stages. Material and methods. The data of a 6-year prospective follow-up of a natural group of initially healthy male workers aged 18-66 years (n = 7,959) were used to determine the predictors of stage I-II RP. For this purpose, a 2×2 confusion matrix and a multivariate regression model were used, and the relative risk factors were assessed. Results. Stage I-II retinopathy was caused by such factors as age from 26 to 66 years, arterial hypertension, smoking, dyslipidemia, hyperglycemia, family hi

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Saputra, Dimas Chaerul Ekty, Elvaro Islami Muryadi, Raksmey Phann, Irianna Futri, and Lismawati Lismawati. "An Innovative Artificial Intelligence-Based Extreme Learning Machine Based on Random Forest Classifier for Diagnosed Diabetes Mellitus." Jurnal Ilmiah Teknik Elektro Komputer dan Informatika 10, no. 1 (2024): 173–87. https://doi.org/10.26555/jiteki.v10i1.28690.

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Since 2014, the World Health Organization has accumulated data indicating that 8.5% of 18-year-olds and older have been diagnosed with diabetes. In 2019, diabetes caused the lives of 1.5 million people worldwide, with those under the age of 70 accounting for 48% of all diabetes-related deaths. It is estimated that diabetes causes an additional 460,000 deaths each year due to renal failure and that hyperglycemia contributes to about 20% of all cardiovascular disease-related deaths. Diabetes may have contributed to a 3% rise in the age-adjusted death rate between the years 2000 and 2019. In rece

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Lazutkina, A. Yu. "Origin and formation of early changes in the retina under the influence of atherosclerosis factors." Ateroscleroz 19, no. 4 (2023): 385–403. http://dx.doi.org/10.52727/2078-256x-2023-19-4-385-403.

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Lesions of the microcirculatory bed of the retina are an urgent problem due to their prevalence, severity of irreversible changes and their association with an unfavorable cardiovascular prognosis. Their pathogenesis is associated with endothelial dysfunction. Determining the processes of the formation and progression of retinopathy (RP) will bring closer the solution of problematic issues in ophthalmology and cardiology, and reduce cardiovascular morbidity and mortality.Aim. To study the processes of formation and progression of retinopathy in the initial stages of mixed origin (hypertensive,

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Lupague, Ryan Marcus Jeremy M., Romie C. Mabborang, Alvin G. Bansil, and Melinda M. Lupague. "Integrated Machine Learning Model for Comprehensive Heart Disease Risk Assessment Based on Multi-Dimensional Health Factors." European Journal of Computer Science and Information Technology 11, no. 3 (2023): 44–58. http://dx.doi.org/10.37745/ejcsit.2013/vol11n34458.

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For a long time, Cardiovascular diseases (CVD) is still one of the leading causes of death globally. The rise of new technologies such as Machine Learning (ML) algorithms can help with the early detection and prevention of developing CVDs. This study mainly focuses on the utilization of different ML models to determine the risk of a person in developing CVDs by using their personal lifestyle factors. This study used, extracted, and processed the 438,693 records as data from the Behavioral Risk Factor Surveillance System (BRFSS) in 2021 from World Health Organization (WHO). The data was then pa

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Palanichamy, Naveen, Su-Cheng Haw, Subramanian S, Rishanti Murugan, and Kuhaneswaran Govindasamy. "Machine learning methods to predict particulate matter PM2.5." F1000Research 11 (April 11, 2022): 406. http://dx.doi.org/10.12688/f1000research.73166.1.

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Introduction Pollution of air in urban cities across the world has been steadily increasing in recent years. An increasing trend in particulate matter, PM2.5, is a threat because it can lead to uncontrollable consequences like worsening of asthma and cardiovascular disease. The metric used to measure air quality is the air pollutant index (API). In Malaysia, machine learning (ML) techniques for PM2.5 have received less attention as the concentration is on predicting other air pollutants. To fill the research gap, this study focuses on correctly predicting PM2.5 concentrations in the smart citi

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Rasitha Banu, N. Sasikala, Amal Ramadan, Thani Babikar, Maha Yousif Rizgalla, and Ashraf Abdelmageid Ibrahim khattab. "Applications of Datamining Techniques for Predicting the Post - Covid 19 Symptoms in Saudi Arabia, Jazan." Journal of Advanced Zoology 44, S6 (2023): 1591–97. http://dx.doi.org/10.17762/jaz.v44is6.2528.

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 Background The entire world was combating COVID-19; however, a significant proportion of patients demonstrate the persistence of some COVID-19 symptoms, new symptom development, or exaggeration of pre-existing disease after a negative viral load. They are referred to as a post-COVID-19 syndrome. According to various researches, COVID-19 has a wide range of long-term effects on virtually all systems, including the respiratory, cardiovascular, gastrointestinal, neurological, mental, and dermatological systems. Finding the various symptoms of post-acute and chronic is critical since they m

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Salih, Hudda, Si Jia Wu, Evgueni Kabakov, Dr. Kang Lee, and Weihong Zhou. "Smartphone-based Identification of Critical Levels of Glycated Hemoglobin A1c using Transdermal Optical Imaging." UTSC's Journal of Natural Sciences 2, no. 1 (2021): 62–72. http://dx.doi.org/10.33137/jns.v2i1.34645.

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Abstract: Worldwide, the prevalence of diabetes has continued to increase rapidly. This gives rise to concerns regarding appropriate diabetes management to ensure optimal glycemic control. Untreated or uncontrolled diabetes can lead to a host of complications, such as cardiovascular diseases, an increased likelihood of morbidity and mortality (Deshpande, Harris-Hayes, & Schootman, 2008). A challenging problem which arises in diabetes management is the limitations of current blood glucose monitoring techniques. Electronic medical devices can potentially overcome the persistent problems in t

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Mahajan, Sahil, Salil Garg, Richa Sharma, et al. "Accuracy of smartphone based electrocardiogram for the detection of rhythm abnormalities in limb lead: a cross sectional study, non-randomised, single blinded and single-center study." International Journal of Research in Medical Sciences 11, no. 4 (2023): 1165–69. http://dx.doi.org/10.18203/2320-6012.ijrms20230855.

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Background: For the identification of arrhythmia and abnormal instances, researchers are examining the reliability of the interpretation offered by smartphone-based portable ECG monitors. The indicator of an unclear alteration in the electrical activity of the heart is a cardiac abnormality. As a result, its early and accurate identification can avoid myocardial infarction and even sudden cardiac death. Objectives of this study were to evaluate and validate the Spandan 12 lead ECG interpretation for accuracy in detection of the cardiac arrhythmias in comparison to the cardiologist diagnosis, a

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Inyang, Saviour, and Imeh Umoren. "From Text to Insights: NLP-Driven Classification of Infectious Diseases Based on Ecological Risk Factors." Journal of Innovation Information Technology and Application (JINITA) 5, no. 2 (2023): 154–65. http://dx.doi.org/10.35970/jinita.v5i2.2084.

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Numerous factors can affect the development of infectious diseases that emerge. While many are the result of natural procedures, such as the gradual emergence of viruses over time, certain ones are the result of human activity. Human activities form an integral part of our ecosystem, and especially the ecological aspect of human activities can encourage disease transmission. Additionally, Health ecologists examine changes in the biological, physical, social, and economic settings to understand how these alterations impact the mental and physical well-being of individuals. Hence, this research

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