Diabetes Mellitus Prediction Based on Enhanced K Strange Points Clustering and Classification

The Diabetes Mellitus prediction system projected in this paper employs the Enhanced K-Strange Points Clustering Algorithm (EKSPCA) and the Naïve Bayes Classifier for clustering and classification respectively. The Enhanced K-Strange Points Clustering Algorithm is employed for its benefits over other clustering algorithms in that it takes lesser time compared to the previously used clustering algorithms, with higher accuracy rate. The outcomes proved that the Diabetes Mellitus prediction system projected in this paper produced better results than the existing systems with respect to execution speed.