[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117842-en":3,"doc-seo-117842-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117842,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","Recent Advancement in Disease Diagnostic Using Machine Learning - Systematic Survey of Decades, Comparisons, and Challenges","Computer-aided diagnosis (CAD) based on machine learning is rapidly expanding in medical imaging, driven by the need to reduce diagnostic errors that can mislead treatment decisions. The review synthesizes machine-learning approaches for disease detection across conditions including hepatitis, diabetes, liver disease, dengue fever, and heart disease, emphasizing how pattern recognition and learning from examples improve precision and decision objectivity. It highlights how dataset characteristics and selected ML methods influence performance, robustness, and the overall decision-making pipeline.","Recent advancement in Disease Diagnostic using machine learning: Systematic survey of decades, comparisons, and challenges  \nFarzaneh Tajdini 1, *, Mohammad-Javad kheiri 2  \n1 Department of Computer Engineering, MalekAshtar University, Tehran, Iran  \n2 Department of Financial Management, Arak Branch, Islamic Azad University, Arak, Iran  \nHighlights  \n• ML techniques have been widely used in the literature as fast, affordable, and non-invasive approaches for CAD prediction.  \n• This paper conducts a comprehensive review of all relevant studies between 1992 and 2019 for ML-based Diagnostic using machine learning.  \n• The impacts of dataset characteristics and applied ML techniques are investigated in detail.  \nABSTRACT  \nComputer-aided diagnosis (CAD), a vibrant medical imaging research field, is expanding quickly. Because errors in medical diagnostic systems might lead to seriously misleading medical treatments, major efforts have been made in recent years to improve computer-aided diagnostics applications. The use of machine learning in computeraided diagnosis is crucial. A simple equation may result in a false indication of items like organs. Therefore, learning from examples is a vital component of pattern recognition. Pattern recognition and machine learning in the biomedical area promise to increase the precision of disease detection and diagnosis. They also support the decision-making process's objectivity. Machine learning provides a practical method for creating elegant and autonomous algorithms to analyze high-dimensional and multimodal bio-medical data. This review article examines machine-learning algorithms for detecting diseases, including hepatitis, diabetes, liver disease, dengue fever, and heart disease. It draws attention to the collection of machine learning techniques and algorithms employed in studying conditions and the ensuing decisionmaking process.  \nKeywords: Disease Diagnostic, Computer-aided diagnosis, Machine Learning, Pattern recognition  \n1. INTRODUCTION  \nThe rapid emergence of enormous amounts of healthcare data will radically alter how medical treatment is provided. Many patients' treatments will be delivered primarily through doctor-patient interaction, strengthened by new insights from machine learning. [68] Machine learning is advantageous because it eliminates the need for explicit human characterization by allowing significant links between different pieces of data to be learned from the data. The ability of a machine learning-based technique to consider a larger variety of facts than a doctor may, at its most basic level, result in a more accurate diagnosis. However, when taken a step further, it allows for a better understanding of the disease (\"Big Data\") by highlighting the patterns in the data. From a scientific/medical research perspective, machine learning is also likely to play a role in aiding clinicians in delivering care to patients.[69]  \nMachine learning is particularly well suited to classification tasks, such as medical image recognition, where the input is a digital image, and the output is binary (\"normal\" or \"disease\") . When identifying previously unseen photos of biopsy-validated lesions, for instance, AI has proven to be more sensitive and specific than dermatologists after initial training in classifying suspected skin lesions as either benign or malignant with input from dermatologists. [70]  \nOne advantage of applying AI to the analysis of medical tests, such as imaging, is that it makes it easier to evaluate tests performed in remote or underserved areas. This can lead to an accurate and timely diagnosis and, if necessary, the ability to refer a patient to expert care at an earlier stage, potentially changing the course of the disease. For instance, remote clinics in many countries with a high tuberculosis prevalence lack radiological expertise [71] . A recent study involving such a system reported that AI (which had been pretrained using active pulmonary tuberculos","cbCaioAU5yAQ9u0n","https://ap.wps.com/l/cbCaioAU5yAQ9u0n","pdf",294412,1,12,"English","en",105,"# Highlights\n# Abstract\n# Keywords\n# Introduction\n# Machine learning","[{\"question\":\"What is the purpose of the review on machine learning for disease diagnosis?\",\"answer\":\"The review comprehensively surveys relevant studies between 1992 and 2019 on ML-based disease diagnosis, focusing on techniques, comparisons, and challenges. It also analyzes how dataset characteristics and applied ML methods affect outcomes.\"},{\"question\":\"Why is machine learning important in computer-aided diagnosis (CAD)?\",\"answer\":\"Machine learning supports more accurate disease detection by learning patterns from data instead of relying solely on explicit human characterization. It also helps decision-making by improving objectivity and enabling analysis of high-dimensional, multimodal biomedical information.\"},{\"question\":\"Which kinds of diseases does the review focus on?\",\"answer\":\"The review examines machine-learning algorithms for detecting hepatitis, diabetes, liver disease, dengue fever, and heart disease, among other diagnostic targets mentioned in the text.\"}]","Recent Advancement in Disease Diagnostic Using Machine Learning - Systematic Survey of Decades, Comparisons, and Challenges | PDF",1785679948,30,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"recent-advancement-in-disease-diagnostic-using-machine-learning-systematic-survey-of-decades-comparisons-and-challenges","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/recent-advancement-in-disease-diagnostic-using-machine-learning-systematic-survey-of-decades-comparisons-and-challenges/117842/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the purpose of the review on machine learning for disease diagnosis?","Question",{"text":75,"@type":76},"The review comprehensively surveys relevant studies between 1992 and 2019 on ML-based disease diagnosis, focusing on techniques, comparisons, and challenges. It also analyzes how dataset characteristics and applied ML methods affect outcomes.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why is machine learning important in computer-aided diagnosis (CAD)?",{"text":80,"@type":76},"Machine learning supports more accurate disease detection by learning patterns from data instead of relying solely on explicit human characterization. It also helps decision-making by improving objectivity and enabling analysis of high-dimensional, multimodal biomedical information.",{"name":82,"@type":73,"acceptedAnswer":83},"Which kinds of diseases does the review focus on?",{"text":84,"@type":76},"The review examines machine-learning algorithms for detecting hepatitis, diabetes, liver disease, dengue fever, and heart disease, among other diagnostic targets mentioned in the text.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":29,"slug":121},"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]