[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123742-en":3,"doc-seo-123742-105":29,"detail-sidebar-cat-0-en-105":90},{"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":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},123742,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","The Significance of Machine Learning in Clinical Disease Diagnosis - A Review","Global demand for accurate clinical disease diagnosis remains high due to complex disease mechanisms and diverse patient symptoms. Increasingly, clinicians and researchers rely on machine learning (ML), a branch of artificial intelligence, to improve diagnostic and treatment decision-making. Existing work still shows limited focus on ML algorithms targeting both accuracy and computational efficiency. This review surveys ML-based disease diagnosis approaches, comparing algorithms, disease targets, data modalities, applications, and evaluation metrics.","The Significance of Machine Learning in Clinical Disease  \nDiagnosis: A Review  \nS. M. Atikur Rahman  \nDepartment of Industrial, Manufacturing and Systems Engineering  \nUniversity of Texas at El Paso El Paso, TX 79968, USA  \nSifat Ibtisum  \nDepartment of Computer Science Missouri University of Science and Technology, Rolla, Missouri  \nEhsan Bazgir  \nDepartment of Electrical Engineering San Francisco Bay University Fremont, CA 94539 , USA  \nTumpa Barai  \nDepartment of CSE European University Bangladesh Dhaka, Bangladesh  \nABSTRACT  \nThe global need for effective disease diagnosis remains substantial, given the complexities of various disease mechanisms and diverse patient symptoms. To tackle these challenges, researchers, physicians, and patients are turning to machine learning (ML), an artificial intelligence (AI) discipline, to develop solutions. By leveraging sophisticated ML and AI methods, healthcare stakeholders gain enhanced diagnostic and treatment capabilities. However, there is a scarcity of research focused on ML algorithms for enhancing the accuracy and computational efficiency. This research investigates the capacity of machine learning algorithms to improve the transmission of heart rate data in time series healthcare metrics, concentrating particularly on optimizing accuracy and efficiency. By exploring various ML algorithms used in healthcare applications, the review presents the latest trends and approaches in ML-based disease diagnosis (MLBDD) . The factors under consideration include the algorithm utilized, the types of diseases targeted, the data types employed, the applications, and the evaluation metrics. This review aims to shed light on the prospects of ML in healthcare, particularly in disease diagnosis. By analyzing the current literature, the study provides insights into state-of-the-art methodologies and their performance metrics.  \nKeywords  \nMachine learning (ML), IoMT, healthcare; supervised learning, chronic kidney disease (CKD), convolutional neural networks, adaptive boosting (AdaBoost), COVID-19, deep learning (DL) .  \n1. INTRODUCTION  \nIn the medical field, artificial intelligence (AI) plays a crucial role in developing algorithms and techniques to aid in disease diagnosis. Medical diagnosis entails determining the illness or conditions that account for an individual's symptoms and indicators, usually relying on their medical background and physical assessment. However, this process can be challenging as many symptoms are ambiguous and require expertise from trained health professionals. This becomes particularly problematic in countries like Bangladesh and India, where there is a scarcity of healthcare professionals, making it difficult to provide proper diagnostic procedures for a large population of patients. Additionally, medical tests required for diagnosis can be expensive and unaffordable for low-income individuals [1-3] .  \nDue to human error, over diagnosis can occur, leading to unnecessary treatment and negatively impacting both the patient's health and the economy. Reports suggest that a significant number of people experience at least one diagnostic mistake during their lifetime. Several factors contribute tomisdiagnosis, including the lack of noticeable symptoms, the presence of rare diseases, and diseases being mistakenly omitted from consideration [4, 5] . ML has found widespread applications in various fields, from cutting-edge technology to healthcare, including disease diagnosis. Its popularity is growing, and it is becoming increasingly utilized in healthcare to improve diagnostic accuracy and safety.  \nML serves as a robust mechanism enabling machines to learn autonomously, eliminating the requirement for explicit programming. It harnesses sophisticated algorithms and statistical methods to analyze data and formulate predictions, departing from traditional rule-based systems. The accuracy of machine learning predictions heavily depends on the quality and relevance of the d","cbCaihVReMgl031g","https://ap.wps.com/l/cbCaihVReMgl031g","pdf",298055,1,"English","en",105,"# 1. INTRODUCTION\n## Challenges in medical diagnosis\n## Role and evolution of machine learning in healthcare\n# 2. AI IN HEALTHCARE AND MEDICINE","[{\"question\":\"Why is machine learning increasingly used in clinical disease diagnosis?\",\"answer\":\"Machine learning can learn patterns from data and support more accurate prediction and classification than rigid rule-based systems. It also improves as more patient data becomes available.\"},{\"question\":\"What does the review focus on regarding ML methods?\",\"answer\":\"The review examines ML-based disease diagnosis approaches with emphasis on accuracy and computational efficiency, covering algorithm choices, targeted diseases, data types, applications, and evaluation metrics.\"},{\"question\":\"What challenges does the document highlight in traditional diagnosis?\",\"answer\":\"Diagnostic processes can be difficult because symptoms are often ambiguous and require expert interpretation, with added constraints such as limited healthcare professionals and expensive tests in some settings. It also notes risks like misdiagnosis leading to unnecessary treatment.\"}]","The Significance of Machine Learning in Clinical Disease Diagnosis - A Review | PDF",1785818283,20,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"the-significance-of-machine-learning-in-clinical-disease-diagnosis-a-review","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/the-significance-of-machine-learning-in-clinical-disease-diagnosis-a-review/123742/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"Why is machine learning increasingly used in clinical disease diagnosis?","Question",{"text":74,"@type":75},"Machine learning can learn patterns from data and support more accurate prediction and classification than rigid rule-based systems. It also improves as more patient data becomes available.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"What does the review focus on regarding ML methods?",{"text":79,"@type":75},"The review examines ML-based disease diagnosis approaches with emphasis on accuracy and computational efficiency, covering algorithm choices, targeted diseases, data types, applications, and evaluation metrics.",{"name":81,"@type":72,"acceptedAnswer":82},"What challenges does the document highlight in traditional diagnosis?",{"text":83,"@type":75},"Diagnostic processes can be difficult because symptoms are often ambiguous and require expert interpretation, with added constraints such as limited healthcare professionals and expensive tests in some settings. 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