A Classification System for Diabetic Patients with Machine Learning Techniques

Abstract

Diabetes mellitus (DM) is a group of metallic disorder characterized by steep levels of blood glucose prolonged over a time. It results the defection in insulin production or improper action of the cells to the insulin produced. It is one of the significant public health care challenge worldwide. Diabetes exists in a body when pancreas does not construct enough hormone insulin or the human body is not being able to use the insulin properly. The diagnosis of diabetes (diagnosis, etiopathophysiology, therapy etc.) need to generate and process the vast amount of data. Data mining techniques have proven its usefulness and effectiveness in order to evaluate the unknown relationships or patterns if exists with such vast data. In the present work, five techniques based on machine learning namely, AdaBoost, LogicBoost, RobustBoost, Naïve Bayes and Bagging have been proposed for the analysis and prediction of DM patients. The proposed techniques are employed on the data set of Pima Indians Diabetes patients. The results computed are found to be very accurate with classification accuracy of 81.77% and 79.69% by bagging and AdaBoost techniques, respectively. Hence, the proposed techniques employed here are highly adorable, effective and efficient in order to predict the DM.

Keywords

• Bagging
• Boosting techniques
• Diabetes mellitus (DM)
• Machine learning techniques
• Naive Bayes Classifier
• RobustBoost techniques
• Prediction