Keywords : Euclidean distance
European Journal of Molecular & Clinical Medicine,
2020, Volume 7, Issue 8, Pages 796-808
Medical domain is revolutionized in terms of Diseases, Diagnosis, and Treatment Prediction thereby undergoing immense pressure due to the high dimensionality of numerable multivariate attributes, suppressing the quality of the analysis. Many techniques like Clustering and Classification have ruled over despite, rendering few hairline gaps towards attaining maximum efficiency. Our Machine Learning-based approach heads towards filling these gaps by adopting advanced K-Means in anticipating Drug likelihood in core attributes of Patients. The proposed Methodology focuses on determining Drug Response similarity by enhanced clustering technique concerning sensitive attributes of Patients. We successfully demonstrated its performance on the UCI Patient dataset reflecting enhanced results concerning Quality Parameters.