{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T23:10:59Z","timestamp":1790464259368,"version":"4.1.0"},"reference-count":26,"publisher":"Wiley","license":[{"start":{"date-parts":[[2022,5,31]],"date-time":"2022-05-31T00:00:00Z","timestamp":1653955200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2022,5,31]]},"abstract":"<jats:p>Fatal diseases like cancer, dementia, and diabetes are very dangerous. This leads to fear of death if these are not diagnosed at early stages. Computer science uses biomedical studies to diagnose cancer, dementia, and diabetes. With the advancement of machine learning, there are various techniques which are accessible to predict and prognosis these diseases based on different datasets. These datasets varied (image datasets and CSV datasets) around the world. So, there is a need for some machine learning classifiers to predict cancer, dementia, and diabetes in a human. In this paper, we used a multifactorial genetic inheritance disorder dataset to predict cancer, dementia, and diabetes. Several studies used different machine learning classifiers to predict cancer, dementia, and diabetes separately with the help of different types of datasets. So, in this paper, multiclass classification proposed methodology used support vector machine (SVM) and K-nearest neighbor (KNN) machine learning techniques to predict three diseases and compared these techniques based on accuracy. Simulation results have shown that the proposed model of SVM and KNN for prediction of dementia, cancer, and diabetes from multifactorial genetic inheritance disorder achieved 92.8% and 92.5%, 92.8% and 91.2% accuracy during training and testing, respectively. So, it is observed that proposed SVM-based dementia, cancer, and diabetes from multifactorial genetic inheritance disorder prediction (MGIDP) give attractive results as compared with the proposed model of KNN. The application of the proposed model helps to prognosis and prediction of cancer, dementia, and diabetes before time and plays a vital role to minimize the death ratio around the world.<\/jats:p>","DOI":"10.1155\/2022\/1051388","type":"journal-article","created":{"date-parts":[[2022,5,31]],"date-time":"2022-05-31T23:20:19Z","timestamp":1654039219000},"page":"1-10","source":"Crossref","is-referenced-by-count":79,"title":["Supervised Machine Learning Empowered Multifactorial Genetic Inheritance Disorder Prediction"],"prefix":"10.1155","volume":"2022","author":[{"given":"Taher M.","family":"Ghazal","sequence":"first","affiliation":[{"name":"School of Information Technology, Skyline University College, Sharjah 1797, UAE"},{"name":"Network and Communication Technology Lab, Center for Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan, Malaysia 43600, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1353-4138","authenticated-orcid":true,"given":"Hussam","family":"Al Hamadi","sequence":"additional","affiliation":[{"name":"Center for Cyber Physical Systems, Khalifa University, Abu Dhabi, 127788, UAE"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad","family":"Umar Nasir","sequence":"additional","affiliation":[{"name":"Riphah School of Computing & Innovation, Faculty of Computing, Riphah International University Lahore Campus, Lahore 54000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6696-277X","authenticated-orcid":true,"family":"Atta-ur-Rahman","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. 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