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Journal articles on the topic 'Liver disease detection'

Author: Grafiati

Published: 5 June 2025

Last updated: 15 July 2025

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1

Mukhopadhyay, Shameek, Subrata Samanta, and Aritra Pan. "Liver Disease Detection." International Journal of Healthcare Information Systems and Informatics 17, no. 1 (2023): 1–22. http://dx.doi.org/10.4018/ijhisi.316666.

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In recent times, intelligent predictive systems are showing greater levels of accuracy and effectiveness in early detection of the critical diseases of cancer in the liver, lungs, etc. Predictive models assist medical practitioners to identify the diseases based on symptoms and health indicators like hormones, enzymes, age, blood counts, etc. This article focuses on proposing an optimal classification model to detect chronic liver disease by enhancing the prediction accuracy through cutting-edge analytics. The article proposes an enhanced framework on the original study by Ramana et al. It use

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Rele, Mayur, and Dipti Patil. "Revolutionizing Liver Disease Diagnosis: AI-Powered Detection and Diagnosis." International Journal of Science and Research (IJSR) 12, no. 11 (2023): 401–7. http://dx.doi.org/10.21275/sr231105021910.

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Kumar V, Dr Saravana. "Early Stage Liver Disease Prediction using Image Processing." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 7397–405. http://dx.doi.org/10.22214/ijraset.2023.52972.

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Abstract: Liver diseases have become increasingly common due to sedentary lifestyles and lack of physical activity, particularly in urban areas and metropolitan cities. This has resulted in millions of deaths every year, with liver cancer being a major contributor. However, inaccurate detection of liver tumors has led to many fatalities. Medical image segmentation is a challenging task when it comes to detecting liver tumors in CT images. Therefore, this project aims to improve the accuracy of tumor detection and segmentation using various image processing techniques, such as pre-processing, e

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Kumar, Dr K. Saravana. "Early Stage Liver Disease Prediction using Image Processing." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 6588–96. http://dx.doi.org/10.22214/ijraset.2023.53197.

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Abstract: Liver diseases have become increasingly common due to sedentary lifestyles and lack of physical activity, particularly in urban areas and metropolitan cities. This has resulted in millions of deaths every year, with liver cancer being a major contributor. However, inaccurate detection of liver tumors has led to many fatalities. Medical image segmentation is a challenging task when it comes to detecting liver tumors in CT images. Therefore, this project aims to improve the accuracy of tumor detection and segmentation using various image processing techniques, such as pre-processing, e

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Gordon, Harriet. "Detection of alcoholic liver disease." World Journal of Gastroenterology 7, no. 3 (2001): 297. http://dx.doi.org/10.3748/wjg.v7.i3.297.

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Seo, Jun-young. "Ultrasound Findings of Diffuse Liver Disease: Basic Steps to Understanding Sonographic Features." Journal of Medical Case Studies 2, no. 1 (2024): 1–8. http://dx.doi.org/10.23880/jmcs-16000110.

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The ultrasound examination, capable of non-invasively detecting subtle changes in the liver's structure and form, can be extremely helpful. It is not only useful in identifying liver tumors but also in diagnosing and differentiating various liver diseases. Continuous monitoring with ultrasound is especially valuable in patients with chronic hepatitis B and C, as well as other chronic liver diseases, for understanding disease progression and early detection of hepatocellular carcinoma. It is also used in determining the degree of liver fibrosis. Therefore, our goal is to enhance diagnostic capa

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Ullah, Rizwan, Aqal Zaman, Arooba Khan, Moiz Inam Khan, Zeeshan Allah Rakha, and Sheikh Atif Mehmood. "Early Detection of Nonalcholic Fatty Liver (NAFLD) on Ultrasound." Pakistan Journal of Medical and Health Sciences 16, no. 8 (2022): 966–69. http://dx.doi.org/10.53350/pjmhs22168966.

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Background: Nonalcoholic fatty liver abnormality is more typical determination in clinical practice and around the world. Obesity and diabetic individuals play a basic role in the development and improvement of this disease. Purpose: This study was done to detect and find out the frequency of nonalcoholic fatty liver abnormalities on ultrasound at Al Noor Ultrasound and KTH hospital Peshawar Pakistan. To review the information of NAFLD and also the hazardous factors for its advancement and the regions where future research is important. Methods: Cross sectional study was done and overall 230 p

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Bushra, Mrs MD. "Liver Disease Detection using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 12, no. 8 (2024): 156–62. http://dx.doi.org/10.22214/ijraset.2024.63879.

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Abstract: Liver disease is a serious worldwide health issue, and prompt diagnosis and treatment are essential for successful outcomes. Traditional diagnostic techniques, however, may be expensive and time-consuming and can require intrusive procedures. In this work, we suggest a machine learning-based method for liver disease identification that makes use of the Support Vector Machine (SVM) and Random Forest Decision Tree algorithms. Our approach uses a large dataset with pertinent clinical characteristics including biochemical signs and patient demographics to categorize people into liver dis

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Testino, Gianni. "Early detection of alcoholic liver disease." Scandinavian Journal of Gastroenterology 51, no. 11 (2016): 1386. http://dx.doi.org/10.1080/00365521.2016.1199732.

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Balistreri, William F. "Detection of CF-associated liver disease." Journal of Pediatrics 143, no. 3 (2003): A3. http://dx.doi.org/10.1067/s0022-3476(03)00521-3.

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Cahyono, Yoyo, Perani Rosyani, Farhan Stiady Syah, Firda Salsabila Putri, Idpan Ashari, and Kurnain Sofian. "Liver Disease Classification Using Decision Tree and Random Forest Algorithms." International Journal of Integrative Sciences 4, no. 1 (2025): 135–40. https://doi.org/10.55927/ijis.v4i1.13509.

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Diagnosing diseases using technology is no longer uncommon. With advancements in healthcare technology, decision-making, particularly in detecting liver diseases, has become more efficient. Liver, an essential human organ, sees its functionality decline in patients with liver diseases. According to WHO data (2013), 28 million individuals in Indonesia suffer from liver diseases, marking it as one of the ten deadliest diseases. Early detection is crucial for effective treatment. This study aims to predict liver diseases using the Random Forest algorithm. Feature selection and classifier choice a

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Kulkarni, Madhavi. "Health Chain: Kidney Liver Disease Diagnosis with Secure Organ Donation Using Blockchain." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem49090.

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Abstract- —This paper presents an integrated framework that leverages blockchain techniques and machine learning for the diagnosis of Kidney Disease and liver diseases. Combining blockchain-based decentralized organ donation systems with advanced machine learning models ensures accurate predictions, secure health record management, and efficient organ donation. We employ statistical feature extraction techniques such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) to improve disease classification, while blockchain ensures the security and transparency of the organ

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Riyan, Bayu Satriya, and Kusnawi Kusnawi. "Random Search Optimization Using Random Forest Algorithm For Liver Disease Prediction." SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan 2, no. 3 (2025): 127–38. https://doi.org/10.5281/zenodo.15468679.

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The liver is a vital human organ with complex and diverse functions. One of the diseases that affect the liver is hepatitis or liver disease. Early detection is crucial to enable more effective intervention and slow the progression of the disease. However, diagnosing liver disease often faces challenges, especially in detecting the early stages of the disease from complex and diverse medical data. This study aims to optimize the <em>Random Forest </em>algorithm using <em>the Random Search</em> method for liver disease detection. <em>The Random Forest </em>algorith

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Biju, Kalyani. "Diagnosis of Chronic Liver Disease using Machine Learning Techniques." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (2023): 346–51. http://dx.doi.org/10.22214/ijraset.2023.53305.

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Abstract: Liver disease is one of the deadly diseases. This issue has been increasing all over the world. Liver disease is a process that may cause the destruction and regeneration of the liver parenchyma. Many diseases and conditions and also use of some drugs may also cause liver diseases. So early detection and treatment can recover the disease in its early stage. Machine learning algorithms like Logistic Regression, KNN and Random Forest algorithms can be applied to detect the liver related diseases. Four stages of liver diseases such as healthy liver, fatty liver, liver fibrosis and liver

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Ladrón Abia, P., B. Sicilia Aladren, J. Hernández Bernad, et al. "P1178 Detection of occult liver disease in patients with Inflammatory Bowel Disease." Journal of Crohn's and Colitis 18, Supplement_1 (2024): i2101. http://dx.doi.org/10.1093/ecco-jcc/jjad212.1308.

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Abstract Background Investigation of liver disease is recommended in the ECCO Guidelines for Extraintestinal Manifestations in Inflammatory Bowel Disease (IBD). The aim of this study was to analyse the prevalence of liver disease in IBD patients, to examine the frequency of different aetiologies, and to investigate a possible correlation between the severity of liver disease and IBD. Methods Cross-sectional descriptive study including all patients with inflammatory bowel disease (IBD) of nine hospitals in Spain. The study of liver disease was carried out in two phases: patients with FIB-4 grea

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Dashti, Naghme, and Elias Ameli Bafandeh. "Liver Lesions Detection and Classification in Ultrasound Images Using Gabor Characterization, Edge Detection and Artificial Neural Networks." Medical Technologies Journal 1, no. 4 (2017): 103–4. http://dx.doi.org/10.26415/2572-004x-vol1iss4p103-104.

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Introduction: In the last decade one of the main reasons for people mortality and disability is liver diseases. Early detection of these diseases can help adopt appropriate treatment methods. Ultrasound imaging is a non-invasive method for visualizing tissue specification and liver lesions detection which its resolution is lower than CT and MRI images. Precise determination of liver tissue lesions and progression degree of disease is possible with advanced computer techniques such as artificial neural networks (ANN) from medical images. In this paper, a classification-based method is presented

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Chakraborty, Subhadeep. "Multi-Disease Detection using Hybrid Machine Learning." Scholars Journal of Engineering and Technology 10, no. 10 (2022): 271–78. http://dx.doi.org/10.36347/sjet.2022.v10i10.002.

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Machine Learning has a significant application in the detection of disease because of the automated process. Using machine learning models, the detection of disease can be done with higher effectiveness and with less error which may be seen in the context of computations made by humans. In this research, the detection of multiple diseases has been done with the application of machine learning. In this research context, three data have been selected namely Heart Disease Data (from UCI Repository), Liver Disease Data (from Kaggle Repository) and Diabetes Data (from Kaggle Repository). To detect

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Pittman, C. Edwin, David H. Buss, and J. Michael Sterchi. "Detection of liver involvement in Hodgkin's disease." Journal of Surgical Oncology 38, no. 4 (1988): 265–66. http://dx.doi.org/10.1002/jso.2930380412.

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Kumaraswamy B, Shirisha Akuthota, Akhila Janam, Kushal Kumar Addanki, and Madhukar Kanukuntla. "Liver Disease Prediction." International Research Journal on Advanced Engineering Hub (IRJAEH) 3, no. 04 (2025): 1231–34. https://doi.org/10.47392/irjaeh.2025.0176.

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The manual and subjective analysis of liver function tests frequently impedes early detection and accurate diagnosis, which are crucial. In order to improve the precision and effectiveness of diagnosis, this work proposes a liver disease prediction model utilizing the Extreme Gradient Boosting (XGBoost) algorithm. Features like Total Bilirubin, Direct Bilirubin, Alkaline Phosphatase, SGOT, SGPT, Albumin, and Albumin-Globulin Ratio are used to train the model on the Indian Liver Patient Dataset (ILPD). Hyperparameter optimization, feature selection, and thorough data preprocessing are used. Hig

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S.D., Bobonazarov, and Norjigitov A.M. "DIAGNOSIS AND TREATMENT OF LIVER CIRRHOSIS." МЕДИЦИНА, ПЕДАГОГИКА И ТЕХНОЛОГИЯ: ТЕОРИЯ И ПРАКТИКА 2, no. 5 (2024): 652–61. https://doi.org/10.5281/zenodo.11399612.

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Chronic liver disease (CLD) is a neglected epidemic. Premature mortality is high, and in Uzbekistan, liver diseases are among the top three causes of inequitable health care, along with heart and respiratory diseases. Fifty percent of patients with CLD are first diagnosed with cirrhosis after emergency care, leading to poor patient outcomes. Traditional care models are based on secondary care when the need is at the community level. Screening patients for the presence of the disease based on population-level risk factors in the community allows early detection of its presence when potential re

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Rokoni, Sakib, Sihab Sarar Chistee, Protik Kanu, Urmi Ghosh, Ashik Ahamed Raian, and Labib Rokoni. "Detection of Nonalcoholic Fatty Liver Disease Using Deep Learning Algorithms." Journal of Computer Science and Technology Studies 5, no. 4 (2023): 150–59. http://dx.doi.org/10.32996/jcsts.2023.5.4.15.

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Some occasional drinkers develop Nonalcoholic Fatty Liver Disease (NAFLD). Hepatocytes are the key indication of NAFLD. Western nations are seeing rising non-alcoholic fatty liver disease (NAFLD). About 25% of Americans have this chronic liver condition. Recent research estimates that 33.66 percent of Bangladeshi adults have fatty liver disease, affecting over 45 million people. This illness is a major cause of liver-related deaths. Thus, minimizing fatty liver disease risk is crucial. Failure to diagnose fatty liver early may cause serious medical consequences. This study examines fatty liver

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Babatunde, Ronke Seyi, Akinbowale Nathaniel Babatunde, Bukola Fatimah Balogun, et al. "A Neuro-Fuzzy-based Approach to Detect Liver Diseases." Pakistan Journal of Engineering and Technology 7, no. 2 (2024): 50–58. http://dx.doi.org/10.51846/vol7iss2pp50-58.

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The liver is a crucial organ in the human body and performs vital functions essential for overall health, including metabolism, immunity, digestion, detoxification, and vitamin storage. Detecting liver diseases at an early stage poses challenges due to the liver's ability to function adequately despite partial damage. Early detection is crucial as liver diseases have significant clinical and socio-economic impacts, affecting other organ systems and requiring timely intervention to improve patient survival rates. Classical diagnostic methods for liver disorders may not always produce better res

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Sadia Latif, Azhar Mehboob, Assad latif, Salahuddin, Muhammad Ramzan, and Muhammad Ans Khalid. "DETECTION OF HCV LIVER FIBROSIS APPLYING MACHINE LEARNING TECHNIQUE." Kashf Journal of Multidisciplinary Research 1, no. 12 (2024): 11–44. https://doi.org/10.71146/kjmr144.

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The health department can be update using the latest technique that will increase the life expectation of the population. Cancer and HCV liver fibrosis are the most dangerous disease in the world due to these diseases death rate is increasing in the world. There are many tools and methodologies exist that predict the spread of HCV liver fibrosis and many performance algorithms applied on the given dataset but what’s the big research gap is still no one used the attributes to find the main reason of HCV liver fibrosis. To overcome this research gap there is a need of best prediction of model by

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Sumallika, Tella, and Raavi Satya Prasad. "HepatoScan: Ensemble classification learning models for liver cancer disease detection." Computer Science and Information Technologies 6, no. 2 (2025): 169–77. https://doi.org/10.11591/csit.v6i2.p169-177.

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Liver cancer is a dangerous disease that poses significant risks to human health. The complexity of early detection of liver cancer increases due to the unpredictable growth of cancer cells. This paper introduces HepatoScan, an ensemble classification to detect and diagnose liver cancer tumors from liver cancer datasets. The proposed HepatoScan is the integrated approach that classifies the three types of liver cancers: hepatocellular carcinoma, cholangiocarcinoma, and angiosarcoma. In the initial stage, liver cancer starts in the liver, while the second stage spreads from the liver to other p

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Shibam Ch. Karmakar, Subham Pratihar, Shreeja Roy, Sulekha Das, and Avijit Chaudhuri. "Early Detection of Liver Disease by using Machine Learning." international journal of engineering technology and management sciences 7, no. 2 (2023): 271–76. http://dx.doi.org/10.46647/ijetms.2023.v07i02.031.

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The liver is the largest internal organ of the human body. It is responsible for conversion of food intake into useful nutrients and also helps to store them. It is responsible for conversion of toxic molecules into harmless particles. But recent studies report significant deaths due to liver diseases. It is mainly due to unhealthy diet habits and unhealthy routine of people. In the race of doing work people are ignoring their health resulting in abnormal health and affecting the liver significantly. Therefore prediction of liver disease with high accuracy and speed is an important concern. Th

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Brajesh Kumar Singh. "A Cloud-Enabled IoT Framework for Liver Disease Detection Using ML and Embedded Electronics." Communications on Applied Nonlinear Analysis 32, no. 5s (2024): 51–68. https://doi.org/10.52783/cana.v32.2947.

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The increasing prevalence of liver diseases worldwide necessitates innovative and efficient diagnostic approaches. This study presents a cloud-enabled Internet of Things (IoT) framework integrating machine learning (ML) algorithms and embedded electronics for real-time liver disease detection. The framework combines wearable sensors and embedded devices to collect vital physiological data, including liver enzyme levels, bilirubin concentration, and patient demographics. These data are transmitted to a cloud-based server through IoT communication protocols, where advanced ML models analyze the

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Standing, Holly C., Helen Jarvis, James Orr, et al. "GPs’ experiences and perceptions of early detection of liver disease: a qualitative study in primary care." British Journal of General Practice 68, no. 676 (2018): e743-e749. http://dx.doi.org/10.3399/bjgp18x699377.

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BackgroundThe incidence of liver disease is increasing in the UK and primary care is a key setting where improvement in the detection and management of liver disease is required. Little is known about GPs’ understanding and confidence in detecting liver disease.AimTo explore GPs’ experiences of liver disease with a focus on early detection and interpretation of liver function tests (LFTs).Design and settingA qualitative study employing semi-structured interviews of a purposive sample of GPs from five UK primary care study sites.MethodTelephone and face-to-face interviews of GPs were undertaken

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Murgia, Antonio, Yusuf Ahmed, Kelly Sweeney, et al. "Breath-Taking Perspectives and Preliminary Data toward Early Detection of Chronic Liver Diseases." Biomedicines 9, no. 11 (2021): 1563. http://dx.doi.org/10.3390/biomedicines9111563.

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The gold standard method for chronic liver diseases diagnosis and staging remains liver biopsy, despite the spread of less invasive surrogate modalities based on imaging and blood biomarkers. Still, more than 50% of chronic liver disease cases are detected at later stages when patients exhibit episodes of liver decompensation. Breath analysis represents an attractive means for the development of non-invasive tests for several pathologies, including chronic liver diseases. In this perspective review, we summarize the main findings of studies that compared the breath of patients with chronic liv

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Khaled, Osama Mohareb, Ahmed Zakareia Elsherif, Ahmed Salama, Mostafa Herajy, and Elsayed Elsedimy. "Evaluating machine learning models for predictive analytics of liver disease detection using healthcare big data." International Journal of Electrical and Computer Engineering (IJECE) 15, no. 1 (2025): 1162. http://dx.doi.org/10.11591/ijece.v15i1.pp1162-1174.

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Liver diseases rank among the most prevalent health issues globally, causing significant morbidity and mortality. Early detection of liver diseases allows for timely intervention, which can prevent the progression of such diseases to more severe stages such as cirrhosis or liver cancer. To this end, many machine learning models have been previously developed to early predict liver diseases among potential patients. However, each model has its accuracy and performance limitations. In this paper, we present a comprehensive comparison of three different machine learning models that can be employe

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Thomas, David L., and Mark S. Sulkowski. "Detection of Liver Disease in Injection Drug Users." Journal of Addictive Diseases 27, no. 2 (2008): 19–24. http://dx.doi.org/10.1300/j069v27n02_03.

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Thijssen, J. "Detection of diffuse liver disease by quantitative echography." Ultrasonic Imaging 14, no. 2 (1992): 193. http://dx.doi.org/10.1016/0161-7346(92)90017-p.

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Gurmeet Kaur Saini. "Review on Liver Cirrhosis Detection using Machine Learning and Deep Learning Techniques." Journal of Information Systems Engineering and Management 10, no. 2s (2025): 186–202. https://doi.org/10.52783/jisem.v10i2s.213.

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Liver illnesses account for further than 2.8% of all fatalities in India each year. However, it can be challenging to spot liver disease in the early stages when the symptoms are minor. The majority of the time, symptoms of liver illness don't appear unless a critical phase has been reached, making it difficult to recognize and diagnose. Therefore, a thorough literature survey is conducted that aims in identifying liver diseases among patients by employing different techniques. The paper starts by discussing briefly about liver and various diseases related to it. Moreover, we have also analyse

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Lazebnik, L. B., and S. V. Turkina. "Possibilities of a new assessment scale for prediction of chronic liver disease “CLivD”." Experimental and Clinical Gastroenterology, no. 11 (March 26, 2024): 62–66. http://dx.doi.org/10.31146/1682-8658-ecg-219-11-62-66.

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The number of patients with chronic liver disease (CLD) is steadily increasing. According to the 2023 update published by the European Association for the Study of the Liver, liver diseases account for two million deaths annually and account for 4% of all deaths (1 in every 25 deaths worldwide). This review emphasizes the need for early detection and control of these diseases course, as a factor that improves the patient's prognosis. It is proposed to use a new scale of the CLivD indicator (Chronic Liver Disease indicator), based on the assessment of risk factors widely available in physician

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Xu, Shi-Xue, Fan Yang, Nan Ge, Jin-Tao Guo, and Si-Yu Sun. "Role of albumin-bilirubin score in non-malignant liver disease." World Journal of Gastroenterology 30, no. 9 (2024): 999–1004. http://dx.doi.org/10.3748/wjg.v30.i9.999.

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The albumin-bilirubin (ALBI) score, which was proposed to assess the prognosis of patients with hepatocellular carcinoma, has gradually been extended to other liver diseases in recent years, including primary biliary cholangitis, liver cirrhosis, hepatitis, liver transplantation, and liver injury. The ALBI score is often compared with classical scores such as the Child-Pugh and model for end-stage liver disease scores or other noninvasive prediction models. It is widely employed because of its immunity to subjective evaluation indicators and ease of obtaining detection indicators. An increasin

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Dr., Divya Sing1 Hina Afaq2 Abdul Hameed3* Yash Srivastav4. "Hepatoprotective Abilities of Flavonoids and the Development and Detection of Hepatic diseases (Liver disease): A Systematic Overview." International Journal of Pharmaceutical Sciences 2, no. 12 (2024): 1801–9. https://doi.org/10.5281/zenodo.14442532.

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Any ailment that affects your liver is referred to as liver disease. Although the causes of these disorders may vary, they can all harm your liver and impair its ability to operate. Your liver is an essential organ that carries out hundreds of metabolic, energy-storage, and waste-filtering functions. It facilitates food digestion, energy conversion, and storage until needed. Additionally, it aids in removing harmful toxins from your circulation. Liver-protective, antioxidant, and anti-inflammatory qualities are significant dietary components, such as flavonoid consumption, in enhancing liver h

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Torres-Valadez, Rafael, Sonia Roman, Alexis Jose-Abrego, et al. "Early detection of liver damage in Mexican patients with chronic liver disease." Journal of Translational Internal Medicine 5, no. 1 (2017): 49–57. http://dx.doi.org/10.1515/jtim-2017-0003.

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AbstractBackground and ObjectiveLiver cirrhosis is usually detected at the later stages of disease. This study is aimed to detect liver damage in patients with chronic liver disease using transitional elastography (TE) and to assess the biochemical parameters associated with liver damage.MethodsIn 578 patients, chronic liver disease based on etiology was diagnosed by clinical and laboratory tests. Liver damage was evaluated with TE (FibroScan®), while its association with biochemical parameters was performed using the logistic regression tests.ResultsOverall, the main etiologies of liver damag

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Mozaffar, Danish, Khursheed Asif, Shamim Ahmed Md, and Sharma Sachin. "Real Time Shear Wave Elastography for Determining the Ideal Site of Liver Biopsy in Diffuse Liver Disease." International Journal of Pharmaceutical and Clinical Research 16, no. 5 (2024): 2966–70. https://doi.org/10.5281/zenodo.13823856.

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<strong>Background: </strong>Diffuse liver disease, which includes chronic hepatitis and NAFLD, requires a precise liver fibrosis assessment to guide treatment and predict outcomes. Liver biopsy is the gold standard for assessing hepatic fibrosis, although it is invasive and prone to sampling mistakes, especially in diffusely afflicted livers. Shear wave elastography (SWE) is a non-invasive imaging technology that measures liver stiffness and may accurately detect liver fibrosis. <strong>Aim: </strong>To evaluate the effectiveness of real-time SWE in determining the ideal site f

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Golubeva, М. D., К. V. Darafeyeva, D. E. Danilau, and D. V. Litvinchuk. "ASSESMENT OF FATIGUE IN PATIENTS WITH CHRONIC LIVER DISEASES." Medical Journal, no. 3(77) (2021): 49–53. http://dx.doi.org/10.51922/1818-426x.2021.3.49.

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To determine the most sensitive questionnaire for the detection of fatigue in patients with chronic liver diseases, 61 patients with chronic liver diseases were inpatient treatment at the City Infectious Diseases Clinical were interviewed. And 72 relatively healthy responses were interviewed using Chronic Liver Disease Questionnaire, the Short Form-36, the Fatigue Assessment Scale. The study was conducted between November 2019 and March 2020. The severity of fatigue and declining quality of life was correlated with the presence of chronic liver diseases, excess body weight, and female sex. The

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Urban, Sabine, Tudor Mocan, Hanna Sänger, Veronika Lukacs-Kornek, and Miroslaw Kornek. "Extracellular Vesicles in Liver Diseases: Diagnostic, Prognostic, and Therapeutic Application." Seminars in Liver Disease 39, no. 01 (2019): 070–77. http://dx.doi.org/10.1055/s-0038-1676122.

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AbstractExtracellular vesicles, comprising exosomes, microvesicles, and apoptotic bodies, represent an emerging field in disease diagnostics and prognosis. They can be isolated from peripheral blood of patients as well as from other body fluids and can therefore be considered a minimally invasive liquid biopsy screening tool. Especially their surface antigen composition can reveal information about disease backgrounds. For several liver diseases, including fatal hepatocellular and cholangiocellular carcinoma as well as other nonmalignant liver disorders such as nonalcoholic fatty liver disease

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Veijola, Lea, Ingrid Nilsson, Leena Halme, et al. "Detection ofHelicobacterspecies in chronic liver disease and chronic inflammatory bowel disease." Annals of Medicine 39, no. 7 (2007): 554–60. http://dx.doi.org/10.1080/07853890701545714.

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Rachna Chaurasia and Sunil. "Radiological approach for early diagnosis of liver parenchymal disease." Asian Journal of Medical Sciences 15, no. 12 (2024): 181–86. https://doi.org/10.71152/ajms.v15i12.4275.

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Background: Diffuse liver parenchymal disease (LPD) encompasses a wide range of liver disorders characterized by extensive involvement of liver tissue, often resulting in significant morbidity and mortality. Early detection and accurate assessment of these diseases are crucial for effective management and improved patient outcomes. Aims and Objectives: This study aims to evaluate the efficacy of various imaging modalities in the early detection and monitoring of diffuse LPDs, specifically focusing on non-invasive techniques such as ultrasonography (USG), computed tomography (CT), and Fibroscan

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Rachna Chaurasia and Sunil. "Radiological approach for early diagnosis of liver parenchymal disease." Asian Journal of Medical Sciences 15, no. 12 (2024): 181–86. https://doi.org/10.3126/ajms.v15i12.68853.

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Background: Diffuse liver parenchymal disease (LPD) encompasses a wide range of liver disorders characterized by extensive involvement of liver tissue, often resulting in significant morbidity and mortality. Early detection and accurate assessment of these diseases are crucial for effective management and improved patient outcomes. Aims and Objectives: This study aims to evaluate the efficacy of various imaging modalities in the early detection and monitoring of diffuse LPDs, specifically focusing on non-invasive techniques such as ultrasonography (USG), computed tomography (CT), and Fibroscan

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Tashkent, Yasmina, John Olynyk, and Alan Wigg. "Liver Disease in Aboriginal and Torres Strait Islander People." Journal of the Australian Indigenous HealthInfoNet 3, no. 4 (2022): 1–27. http://dx.doi.org/10.14221/aihjournal.v3n4.5.

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Aboriginal and Torres Strait Islander people have a substantially higher prevalence of liver disease than non-Indigenous Australians. Cirrhosis and its complications were the sixth leading cause of mortality for Aboriginal and Torres Strait Islander people in 2020. Liver disease has been estimated to be the third leading cause of the mortality gap between Aboriginal and Torres Strait Islander and non-Indigenous people due to chronic disease, accounting for 11% of this gap. While current trends show reducing mortality rates for Aboriginal and Torres Strait Islander people for conditions includi

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Maheshwari, Sharad, Sachin Kumar, Bharatbhai V. Nakshiwala, et al. "Fatty Liver Disease: Pathophysiology and Imaging Features." Indographics 01, no. 01 (2022): 057–77. http://dx.doi.org/10.1055/s-0042-1742574.

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AbstractFatty liver is a benign condition to start with and is characterized by excess triglyceride in the hepatocytes. However, in the long term, it can lead to increased oxidative stress & inflammation, with resultant steatohepatitis. This can subsequently progress to cirrhosis and eventually an increased risk of developing hepatocellular carcinoma (HCC). Liver biopsy is the gold standard for quantification of fat and assessing the degree of fibrosis, however, it is invasive and cannot be applied to a wider patient population. Conventional modalities like ultrasound offer a qualitative a

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Madadi-Sanjani, Omid, Gunnar Bohlen, Fabian Wehrmann, et al. "Increased Serum Levels of Activated Caspases in Murine and Human Biliary Atresia." Journal of Clinical Medicine 10, no. 12 (2021): 2718. http://dx.doi.org/10.3390/jcm10122718.

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In biliary atresia (BA), apoptosis is part of the pathomechanism, which results in progressive liver fibrosis. There is increasing evidence suggesting that apoptotic liver injury can be non-invasively detected by measuring the caspase activity in the serum. The purpose of this study was to investigate whether serological detection of caspase activation mirrors apoptotic liver injury in the infective murine BA-model and represents a suitable biomarker for BA in humans. Analysis showed increased caspase-3 activity and apoptosis in the livers of cholestatic BALB/c mice, which correlated significa

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Zhang, Jiawei, Dandan Li, Rui Zhang, Peng Gao, Rongxue Peng, and Jinming Li. "The miR-21 potential of serving as a biomarker for liver diseases in clinical practice." Biochemical Society Transactions 48, no. 5 (2020): 2295–305. http://dx.doi.org/10.1042/bst20200653.

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The role of miR-21 in the pathogenesis of various liver diseases, together with the possibility of detecting microRNA in the circulation, makes miR-21 a potential biomarker for noninvasive detection. In this review, we summarize the potential utility of extracellular miR-21 in the clinical management of hepatic disease patients and compared it with the current clinical practice. MiR-21 shows screening and prognostic value for liver cancer. In liver cirrhosis, miR-21 may serve as a biomarker for the differentiating diagnosis and prognosis. MiR-21 is also a potential biomarker for the severity o

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Sheron, Nick, Michael Moore, Wendy O’Brien, Scott Harris, and Paul Roderick. "Feasibility of detection and intervention for alcohol-related liver disease in the community: the Alcohol and Liver Disease Detection study (ALDDeS)." British Journal of General Practice 63, no. 615 (2013): e698-e705. http://dx.doi.org/10.3399/bjgp13x673711.

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Villanueva Raisman, Andrea, David Kotol, Ozlem Altay, et al. "Advancing Chronic Liver Disease Diagnoses: Targeted Proteomics for the Non-Invasive Detection of Fibrosis." Livers 5, no. 1 (2025): 2. https://doi.org/10.3390/livers5010002.

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Chronic liver disease poses significant challenges to healthcare systems, which frequently struggle to meet the needs of end-stage liver disease patients. Early detection and management are essential because liver damage and fibrosis are potentially reversible. However, the implementation of population-wide screenings is hindered by the asymptomatic nature of early chronic liver disease, along with the risks and costs associated with traditional diagnostics, such as liver biopsies. This study pioneers the development of innovative, minimally invasive methods capable of improving the outcomes o

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Hamid Mohammed, Nadia. "Morphological and Molecular Detection of Dicrocoelium dendriticum Infection in Ruminants." Iranian Journal of Veterinary Medicine 18, no. 1 (2024): 51–58. http://dx.doi.org/10.32598/ijvm.18.1.1005360.

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Background: Dicrocoeliosis is a parasite disease that affects the liver of domestic and wild ruminants. It is one of the challenges in the livestock industry that causes significant diseases and economic losses. Objectives: This study aimed to identify Dicrocoelium dendriticum in sheep and cattle using their morphological and molecular characteristics. Methods: The current study examined 122 sheep and cattle livers from both sexes slaughtered in butcher shops. Found worms were selected randomly from the livers of sheep and cattle. They were stained for parasite morphometric measurements studie

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Shirly Gunawan, Alicia Sarjuwita, Vini Claudya Agustine Rajagukguk, and Yohanes Firmansyah. "Kegiatan Pengabdian Masyarakat dalam Rangka Peningkatan Pemahaman Masyarakat Tentang Penyakit Perlemakan Hati dan Deteksi Dini Penyakit Liver." Jurnal Pengabdian Ilmu Kesehatan 3, no. 2 (2023): 50–59. http://dx.doi.org/10.55606/jpikes.v3i2.1819.

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Non-alcoholic fatty liver disease has become a health problem increasing worldwide. This disease is characterized by excessive fat accumulation in the liver cells unrelated to alcohol consumption. Education and risk factor detection are essential in identifying and preventing the development of this disease. This community service involved 52 respondents at Kalam Kudus II Jakarta High School in May 2023. All participants participated in a series of activities starting from community counseling, disease detection, and education. The results of the medical examination revealed that there were 2

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