Application of Two Machine Learning Techniques for Predicting and Classifying the Type II Diabetes mellitus

Abstract

The main objective of the study is to evaluate the prediction and the classification accuracy of two Supervised Machine Learning Techniques which are linear discriminant analysis (LDA) and logistic regression analysis (LRA) using real data of Type II diabetes. The classification accuracy for both models was determined by the classification accuracy rate. LRA and LDA correctly classified 78.70%and 80.00% of the Type II diabetes mellitus (diabetics and non-diabetics) respectively. The LRA has sensitivity and specificity was 64.38% and 85.35% respectively and the LDA had a sensitivity and specificity of 70.88% and 84.77% respectively. Both algorithms had a good overall classification rate. In terms of proper classification rate, the LDA model slightly outperformed the LRA approach. In general, the findings of this study revealed that the LRA model appears to be appropriate for prediction accuracy while the LDA model appears to be appropriate for classification procedures.