Performance Evaluation of Various Classifiers for Diabetes Detection: A Comparative Approach

Abstract

Various Supervised learning algorithms or techniques viz. Random Forest, Naïve Bayes Classifier, Logistic
Regression (LR), K-Nearest Neighbour (KNN) algorithm, Support Vector Machines (SVM), etc are used for the
purpose of data classification.. But the question is which of the classification technique accurately identifies this
sensitive disorders like Diabetes. The accuracy, specificity and sensitivity, are some of the important performance
evaluation parameters, which are required to be analysed for every machine learning algorithm. In the performed
work, the various classification techniques viz. (NB) Naïve Bayes Classifier, (LR) Logistic Regression, (KNN)
K-Nearest Neighbour algorithm, (SVM) Support Vector Machines, and (RF) Random Forest are compared on
the basis of the accuracy, sensitivity and specificity as the performance evaluation parameters. The classifiers
were exposed to the Pima Indian dataset for classification of diabetes, and their respective performance metrics
Accuracy, Sensitivity, and Specificity were compared. It is found that on account of accuracy sensitivity and
specificity the Random Forest performed the best on the Pima Indian Dataset for the Diabetes detection.