An Empirical Comparison of Supervised Classifiers for Diabetic Diagnosis

S. Jahangeer Sidiq, Dr. Majid Zaman, Mudasir Ashraf, Dr. Muheet Ahmed


The focus of this paper is on diagnosing the diabetes using different supervised machine learning classifiers such as Neural Networks, SVM, KNN, Naïve Bayes technique and Decision trees using holdout validation. The diabetic dataset classification is one of the research problems of machine learning research community. The pima Indian diabetes dataset which is available at [1] UCI machine learning repository has been used in all the experiments mentioned in this paper and MATLAB 2014 has been used to perform the experiments. Here we have mainly focus on the performance evaluation methods like accuracy, error rate, sensitivity, specificity, confusion matrix and AUC. Keywords: Diabetes, Diagnosis, Classification, Accuracy, Machine learning.

Full Text:




  • There are currently no refbacks.

Copyright (c) 2017 International Journal of Advanced Research in Computer Science