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Random tree, support vector machine, and gradient booster are just a few proposed ML classifiers. Artificial neural networks (ANNs) have been trained using a variety of medical datasets to predict and analyze outcomes. ML-CANNM collects patient data from various studies and uses ML and ANNs to determine the results. Three layers make up an ANN. ML is used to classify the given patients\u2019 data in the input layer. In the hidden layer, classification data are compared to a training dataset. The output layer\u2019s job is to identify, classify, and diagnose diseases. As a result, disease diagnosis and detection are integrated into a single healthcare database. The proposed framework has proven that ML-CANNM works with more accuracy and lesser execution time. Thus, the numerical outcome suggested ML-CANNM increased accuracy ratio of 99.2% and a prediction ratio of 97.5%. The findings further show that the execution time is enhanced by less than 2[Formula: see text]h, decision table using ML and results in an efficiency ratio of 97.5%. <\/jats:p>","DOI":"10.1142\/s0218001422400079","type":"journal-article","created":{"date-parts":[[2022,12,15]],"date-time":"2022-12-15T05:26:52Z","timestamp":1671082012000},"source":"Crossref","is-referenced-by-count":3,"title":["Artificial Neural Network-Based Medical Diagnostics and Therapeutics"],"prefix":"10.1142","volume":"36","author":[{"given":"Mohammed Hasan","family":"Ali","sequence":"first","affiliation":[{"name":"Computer Techniques Engineering Department, Faculty of Information Technology, Imam Ja\u2019afar Al-Sadiq University, Najaf 10023, Iraq"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4416-2162","authenticated-orcid":false,"given":"Mustafa Musa","family":"Jaber","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Al-Turath University College, Baghdad, Iraq"},{"name":"Department of Medical Instruments Engineering Techniques, Al-Farahidi University, Baghdad, Iraq"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sura Khalil","family":"Abd","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Dijlah University College, Baghdad 10021, Iraq"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ahmed","family":"Alkhayyat","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering Techniques, College of Technical Engineering, The Islamic University, Najaf, Iraq"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abdali Dakhil","family":"Jasim","sequence":"additional","affiliation":[{"name":"English Language Department, Al-Mustaqbal University College, Hillah 51001, Iraq"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2023,2,23]]},"reference":[{"issue":"1","key":"S0218001422400079BIB001","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1109\/MNET.011.2000069","volume":"35","author":"Aggarwal S.","year":"2021","journal-title":"IEEE Netw."},{"key":"S0218001422400079BIB002","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1007\/978-3-030-57024-8_15","volume-title":"Machine Intelligence and Big Data Analytics for Cybersecurity Applications","author":"Ahmad U.","year":"2021"},{"key":"S0218001422400079BIB003","doi-asserted-by":"publisher","DOI":"10.1016\/j.comcom.2019.10.004"},{"key":"S0218001422400079BIB004","doi-asserted-by":"crossref","first-page":"102589","DOI":"10.1016\/j.scs.2020.102589","volume":"65","author":"Bhattacharya S.","year":"2021","journal-title":"Sustain. 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