(untitled) (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021)
Because of the computational complexity of numerous to count multivariate attributes, the medical realm is revolutionized in terms of Diseases, Diagnosis, and Treatment Prediction, putting tremendous emphasis on the consistency of the study. Despite this, many methods such as Clustering and Classification have dominated the day, leaving just a few hairline holes on the road to full productivity. By using advanced K-Means in predicting Drug probability in core characteristics of Patients, our Deep Learning-based solution aims to close these holes. The suggested Methodology focuses on assessing Drug Response Similarities using an improved clustering approach that takes into account sensitive patient characteristics. We conclusively achieved its accuracy on the UCI Patient dataset, with improved Quality Variable outcomes.