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10.3390/healthcare10030541 http://scihub22266oqcxt.onion/10.3390/healthcare10030541 35327018!8950225!35327018     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Healthcare+(Basel) 2022 ; 10 (3): ä Nephropedia Template TP {{tp|p=35327018|t=2022. Machine-Learning-Based Disease Diagnosis: A Comprehensive Review |pdf=|usr=}}{{35327018}} gab.com Text Machine-Learning-Based Disease Diagnosis: A Comprehensive Review JO: Healthcare (Basel) http://www.kidney.de/pget.php?&tw=1&pmid=35327018 #FOAvip Globally, there is a substantial unmet need to diagnose various diseases effectively. The complexity of the different disease mechanisms and underlying symptoms of the patient population presents massive challenges in developing the early diagnosis tool and effective treatment. Machine learning (ML), an area of artificial intelligence (AI), enables researchers, physicians, and patients to solve some of these issues. Based on relevant research, this review explains how machine learning (ML) is being used to help in the early identification of numerous diseases. Initially, a bibliometric analysis of the publication is carried out using data from the Scopus and Web of Science (WOS) databases. The bibliometric study of 1216 publications was undertaken to determine the most prolific authors, nations, organizations, and most cited articles. The review then summarizes the most recent trends and approaches in machine-learning-based disease diagnosis (MLBDD), considering the following factors: algorithm, disease types, data type, application, and evaluation metrics. Finally, in this paper, we highlight key results and provides insight into future trends and opportunities in the MLBDD area. Twit Text FOAVip Machine-Learning-Based Disease Diagnosis: A Comprehensive Review ????Healthcare (Basel) http://www.kidney.de/pget.php?&tw=1&pmid=35327018 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=35327018 #FOAvip English Wikipedia <ref>{{Cite pmid|35327018}}</ref> Machine-Learning-Based Disease Diagnosis: A Comprehensive Review #MMPMID35327018 Ahsan MM; Luna SA; Siddique Z Healthcare (Basel) 2022[Mar]; 10 (3): ä PMID35327018show ga Globally, there is a substantial unmet need to diagnose various diseases effectively. The complexity of the different disease mechanisms and underlying symptoms of the patient population presents massive challenges in developing the early diagnosis tool and effective treatment. Machine learning (ML), an area of artificial intelligence (AI), enables researchers, physicians, and patients to solve some of these issues. Based on relevant research, this review explains how machine learning (ML) is being used to help in the early identification of numerous diseases. Initially, a bibliometric analysis of the publication is carried out using data from the Scopus and Web of Science (WOS) databases. The bibliometric study of 1216 publications was undertaken to determine the most prolific authors, nations, organizations, and most cited articles. The review then summarizes the most recent trends and approaches in machine-learning-based disease diagnosis (MLBDD), considering the following factors: algorithm, disease types, data type, application, and evaluation metrics. Finally, in this paper, we highlight key results and provides insight into future trends and opportunities in the MLBDD area. äMachine-Learning-Based Disease Diagnosis: A Comprehensive Review (epmc).pdf DeepDyve Pubget Overpricing