[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126115-en":3,"doc-seo-126115-105":31,"detail-sidebar-cat-0-en-105":93},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126115,5909887254083,"Miles","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",7,"Healthcare","Current critical review on prediction stroke using machine learning","Strokes are a major health burden due to delayed diagnosis and limited disease information, often resulting in long-term disability. Artificial intelligence, particularly machine learning and deep learning, supports ischemic and hemorrhagic stroke prediction and improves diagnostic assistance. Using PRISMA, 79 relevant articles from five databases (2012–2022) were analyzed, with IEEE contributing the most papers and citations. Random forest achieved the highest accuracy, and a five-area taxonomy (building, system planning, evaluation, comparison, analysis) was developed. Further work should explore additional stroke-related features and enable decentralized federated learning for remote data collection and unified training for early diagnosis.","Current critical review on prediction stroke using machine  \nlearning  \nAgus Byna1,2, Muhammad Modi Lakulu1, Ismail Yusuf Panessai1  \n1Faculty of Computing and Meta-Technology, Universiti Pendidikan Sultan Idris, Perak, Malaysia 2Department of Information Systems, Faculty of Science and Technology, Universitas Sari Mulia, Kalimantan Selatan, Indonesia  \nArticle history:  \nReceived Aug 18, 2023 Revised Feb 13, 2024 Accepted Feb 24, 2024  \nKeywords:  \nArtificial intelligence Deep learning Hemorhargic Ischemic  \nMachine learning Stroke  \nCorresponding Author:  \nStrokes are a significant health problem because they often lead to long-term disabilities due to delayed diagnoses and insufficient information about the disease. The use of artificial intelligence (AI), specifically machine learning (ML) and deep learning (DL), has the potential to aid in stroke diagnosis and significantly advance healthcare. This review article critically examines predictive methods for ischemic and hemorrhagic strokes. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) method was used to identify 79 relevant articles from five databases spanning 2012 to 2022, with IEEE having the highest number of articles and citations. China had the most authors, and the random forest (RF) algorithm showed the most accurate results. A taxonomy categorizing the implementation and usage of ML and DL for stroke prediction was created and includes five focus areas: building, system planning, evaluation, comparison, and analysis. Additional research into other disease features related to stroke is warranted. Decentralized federated learning should also be implemented to collect data from remote locations for early diagnosis and create a single training model.  \nThis is an open access article under the CC BY-SA license.  \nMuhammad Modi Lakulu  \nFaculty of Computing and Meta-Technology, Universiti Pendidikan Sultan Idris 35900 Perak, Malaysia  \n[Email: modi@meta.upsi.edu.my](Email: modi@meta.upsi.edu.my)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nStroke is a significant burden in developing countries, leading to increased mortality rates in Asia,  \nEurope, and the Americas. Healthcare providers face challenges disseminating information about stroke, which can delay treatment [1]–[3] . Early diagnosis is critical to prevent stroke and optimize treatment outcomes [4]–[6] . Artificial intelligence (AI) methods, particularly machine learning (ML) and deep learning (DL), are crucial in reducing the incidence of stroke [7], [8] .  \nSeveral researchers [9]–[12] incorporated various variables and observations into a predictive framework without pre-programmed rules, potentially increasing interest in using ML to predict stroke outcomes. ML offers an alternative for large-scale [13] and multi-institutional data and can optimize the selection process for endovascular treatment versus medical treatment in managing acute stroke [14] . Research conducted by Singh and Choundhary [15] describes an integrated ML and data mining approach to build predictive models, identifying new potential factors for stroke [16] .  \nComparing different methods for stroke prediction on datasets using decision trees (DT) [17], principal component analysis [18], and artificial neural network (ANN) classification algorithms has resulted in more accurate classification models [19] . However, the traditional medical personnel approach to predicting stroke must include identification [20], and effective methods are needed to reduce this impact [21] . In addition, data imbalance between classes in a dataset can affect prediction bias and degrade model  \nperformance [22] . Applying data-driven [23] and model-driven methods can improve their performance by training the data to be better [24] .  \nA systematic literature review concluded that several ML and DL models have been developed to solve stroke cases. Such as predicting stroke-related mortality [25] and patient dependence on stro","cbCaipmZR9iwkc1f","https://ap.wps.com/l/cbCaipmZR9iwkc1f","pdf",514022,9,1,11,"English","en",105,"# Introduction\n## Stroke as a health burden and need for early diagnosis\n## Role of AI, ML, and DL in predictive modeling\n## Challenges: data imbalance and reporting standards\n# Systematic review approach and taxonomy\n## PRISMA-based article identification\n## Five-category taxonomy for ML/DL prediction methods\n## Model evaluation, comparison, and analysis\n# Future directions","[{\"question\":\"What problem does the review address regarding stroke care?\",\"answer\":\"Stroke often leads to long-term disability because diagnoses can be delayed and information about the disease may be insufficient.\"},{\"question\":\"How was the literature search conducted in the review?\",\"answer\":\"The review used the PRISMA method to identify 79 relevant articles from five databases spanning 2012 to 2022.\"},{\"question\":\"What algorithm and taxonomy outcomes are highlighted?\",\"answer\":\"Random forest showed the most accurate results, and the authors created a taxonomy covering five focus areas: building, system planning, evaluation, comparison, and analysis.\"}]","Current critical review on prediction stroke using machine learning | 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