aspg-21-1830-1791468693 20261008141133 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group International Journal of Neutrosophic Science IJNS 2692-6148 2690-6805 2023 21 2 A. Bermúdez del Sol Universidad Regional Autónoma de los Andes, Ecuador Edison Sotalin Nivela Universidad Regional Autónoma de los Andes, Ecuador Edwin Miranda Solis Universidad Regional Autónoma de los Andes, Ecuador Yasser H. Elawady Computer Engineering Department, Misr Higher Institute for Engineering and Technology, Mansoura, Egypt Because of its far-reaching effects, diabetes remains a major health problem on a worldwide scale. It's a metabolic illness that causes hyperglycemia and a host of other health issues, including cardiovascular disease, renal failure, and neuropathy. Many scientists have spent time and energy over the years trying to develop a reliable diabetes prediction model. Researchers are forced to adopt big data analytics and machine learning (ML)-based methodologies since there are still major open research concerns in this area owing to a lack of acceptable data sets and prediction techniques. This study seeks solutions by way of an examination of healthcare predictive analytics. The major purpose of this research was to explore the potential applications of big data analytics and machine learning-based approaches in the field of diabetes. In this study, we used the neutrosophic AHP as a feature selection method. The neutrosophic AHP is used to compute the importance of features, then apply the machine learning methods to these features. This study applied logistic regression, support vector machine (SVM), and random forest (RF) to predict the disease of diabetes. 2023 75 83 1830 https://creativecommons.org/licenses/by/4.0/ 10.54216/IJNS.210207 https://www.americaspg.com/journal/21/article/1830 https://www.americaspg.com/storage/31686134532.pdf