Comparative Analysis of KNN and SVM Algorithms for Diabetes Prediction

Diabetes Mellitus is a chronic, multifactorial syndrome related to defective insulin secretion or its action. This can be considered a disease of all people in the world; hence, early detection of this disease will provide a better outcome in terms of prevention and improvement of patients. This also follows the comparative analysis between K-Nearest Neighbors and the Support Vector Machine on the Pima Indians Diabetes Dataset, consisting of 768 instances with eight quantitative attributes along with a binary target attribute. Cleaning the data, handling the class imbalance by using SMOTE, and scaling the features are the preprocessing steps involved in the exploration of data. After the training and evaluation of the model, it was found that 80% of the data was used for training, and the remaining 20% were used for testing. From these, the results proved very dominantly that SVM holds a very strong position over KNN in nearly all performance metrics. Here, the accuracy of SVM is about 89.45%, while that of KNN is approximately 78.57%. The confusion matrix graph also showed very minimum false positives and false negatives, hence developed more accurate predictions about diabetes. To this end, it would be desirable that future research investigate the inclusion of ensemble techniques for more advanced and heuristic diagnostic tools in healthcare that may further assist in real-time and cost-effective solutions for improved global diabetes management or good health outcomes.