[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118991-en":3,"doc-seo-118991-105":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},118991,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","A Comprehensive Review on Machine Learning Based Models for Healthcare Applications","This review synthesizes how artificial intelligence and machine learning advances are applied in the medical sector, emphasizing the role of ML models in extracting patterns from unprocessed health data. It organizes machine learning approaches for healthcare using a taxonomy across data preparation strategies (e.g., data cleansing and compression), learning paradigms (reinforcement, semi-supervised, supervised, unsupervised), and evaluation methods (simulation-based and real-world assessment). It further maps these ML applications to diagnosis and treatment, then surveys representative research, highlighting challenges, limitations, and future directions for investigators.","A Comprehensive Review on Machine Learning Based Models for Healthcare Applications  \nSabari Vasan S1  \n1Research Scholar  \nSchool of Computer Science Engineering and Information Systems  \nVellore Institute of Technology  \nVellore, India  \n[sabarivasan.s2022@vitstudent.ac.in](sabarivasan.s2022@vitstudent.ac.in)  \nJayalakshmi P2  \n2Assistant Professor  \nSchool of Computer Science Engineering and Information Systems  \nVellore Institute of Technology  \nVellore, India  \n[pjayalakshmi@vit.ac.in](pjayalakshmi@vit.ac.in)  \nAbstract—At present, there has been significant progress concerning AI and machine learning, specifically in medical sector. Artificial intelligence refers to computing programmes that replicate and simulate human intelligence, such as an individual's problem-solving capabilities or their capacity for learning. Moreover, machine learning can be considered as a subfield within the broader domain of artificial intelligence. The process automatically identifies and analyses patterns within unprocessed data. The objective of this work is to facilitate researchers in acquiring an extensive knowledge of machine learning and its utilisation within the healthcare domain. This research commences by providing a categorization of machine learning-based methodologies concerning healthcare. In accordance with the taxonomy, we have put forth, machine learning approaches in the healthcare domain are classified according to various factors. These factors include the methods employed for the process of preparing data for analysis, which includes activities such as data cleansing and data compression techniques. Additionally, the strategies for learning are utilised, such as reinforcement learning, semi-supervised learning, supervised learning, and unsupervised learning. are considered. Also, the evaluation approaches employed encompass simulation-based evaluation as well as evaluation of actual use in everyday situations. Lastly, the applications of these ML-based methods in medicine pertain towards diagnosis and treatment. Based on the classification we have put forward; we proceed to examine a selection of research that have been presented in the framework of machine learning applications within the healthcare domain. This review paper serves as a valuable resource for researchers seeking to gain familiarity with the latest research on ML applications concerning medicine. It aids towards the recognition for obstacles and limitations associated with ML in this domain, while also facilitating the identification of potential future research directions.  \nKeywords-Artificial Intelligence; Machine Learning; Diagnosis; Treatment; Healthcare  \nI. INTRODUCTION  \nFrom the start of the modern era, there was a substantial rise in the significance attributed to technological advances in regards of both productivity and expansion [1–3]. This tendency is anticipated to persist. Kaplan and Haenlein's work [4] claims that improvements in machine technology have replaced manual labor-intensive tasks, which has aided in human development. Artificial intelligence has become a pivotal technological innovation that enables individuals in different sectors to substitute physical labour with enhanced mental capabilities and cognitive abilities [5,6].The application of AI in the medical industry employs computational methods to extract meaningful insights from unprocessed data enabling  \naccurate and precise decision-making in the field of medicines [7, 8]. The field of machine learning represents a subfield within the broader discipline of artificial intelligence. The system can autonomously identify and uncover trends inside datasets. Machine learning models possess the ability to acquire knowledge and refine their performance through automated learning processes without requiring detailed programming instructions [9, 10] . Basically, a learning framework acquires knowledge by analysing tests, but specific programming conforms to established regulatio","cbCaidnXECjmq1Rf","https://ap.wps.com/l/cbCaidnXECjmq1Rf","pdf",665695,1,23,"English","en",105,"# Introduction\n## Background and Motivation\n## AI and ML in Healthcare\n## Data Sources and Collection Methods\n## Early Decision-Making Systems","[{\"question\":\"What is the main objective of the review?\",\"answer\":\"The review aims to help researchers build comprehensive knowledge of machine learning and its utilization in the healthcare domain, including current research trends and future directions.\"},{\"question\":\"How does the paper categorize machine learning methodologies for healthcare?\",\"answer\":\"It classifies approaches by data preparation methods (such as cleansing and compression), learning strategies (reinforcement, semi-supervised, supervised, unsupervised), evaluation approaches (simulation vs. real-world), and healthcare use cases.\"},{\"question\":\"Where are ML-based methods applied in medicine according to the review?\",\"answer\":\"The review states that ML-based methods in healthcare are directed toward diagnosis and treatment.\"}]","A Comprehensive Review on Machine Learning Based Models for Healthcare Applications | 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is the main objective of the review?","Question",{"text":75,"@type":76},"The review aims to help researchers build comprehensive knowledge of machine learning and its utilization in the healthcare domain, including current research trends and future directions.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the paper categorize machine learning methodologies for healthcare?",{"text":80,"@type":76},"It classifies approaches by data preparation methods (such as cleansing and compression), learning strategies (reinforcement, semi-supervised, supervised, unsupervised), evaluation approaches (simulation vs. real-world), and healthcare use cases.",{"name":82,"@type":73,"acceptedAnswer":83},"Where are ML-based methods applied in medicine according to the review?",{"text":84,"@type":76},"The review states that ML-based methods in healthcare are directed toward diagnosis and 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