aspg-21-1829-1791468690 20261008141130 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group International Journal of Neutrosophic Science IJNS 2692-6148 2690-6805 2023 21 2 Piedad Acurio Padilla Universidad Regional Autónoma de los Andes, Ecuador Evelyn Betancourt Rubio Universidad Regional Autónoma de los Andes, Ecuador Walter Vayas Valdiviezo Universidad Regional Autónoma de los Andes, Ecuador Mohammed K. Hassan Mechatronics department, Faculty of Engineering, Horus university-Egypt (HUE), Egypt Heart disease, often known as cardiovascular illness, encompasses a broad range of heart-related disorders and has emerged as the leading cause of mortality during the last few decades everywhere in the globe. Numerous hazards are linked to cardiovascular disease, and timely, effective, and practical methods for making an early diagnosis are required for effective and efficient treatment. In this study, we describe a novel clustering technique for data that is unreliable clustering called neutrosophic c-means (NCM), which draws inspiration from both fuzzy c-means and the neutrosophic set architecture. The NCM is used to predict heart disease. There are four different databases included in the collection, all of which were created in 1988: Cleveland, Hungary, Switzerland, and Long Beach V. There are 76 qualities total, such as the anticipated characteristic, however only 14 have been used in any of the published trials. 2023 68 74 1829 https://creativecommons.org/licenses/by/4.0/ 10.54216/IJNS.210206 https://www.americaspg.com/journal/21/article/1829 https://www.americaspg.com/storage/51686132596.pdf