Analysis of Classifiers for Prediction of Type II Diabetes Mellitus

Abstract

Diabetes mellitus is a chronic disease and a health challenge all over the world. As per the International Diabetes Federation, 451 million people have diabetes worldwide and this number is expected to rise up to 693 million people by 2045. It has been shown that 80% of the complications arising from type II Diabetes can be prevented or delayed by early identification of people at risk. Diabetes is difficult to diagnose in the early stages as the symptoms of the disease grow subtly and gradually. Many of the cases involve the patient being undiagnosed until they are admitted for a heart attack or begin to lose their sight. This paper analyzes the different classification algorithms based on a patient's health history to aid doctors identify the presence as well as promote early diagnosis and treatment. The experiments were conducted on Pima Indian Diabetes data set. Various classifiers used include K Nearest Neighbors, Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, Support Vector Machine and Neural network. Results demonstrate that random forests performed well on the data set giving an accuracy of 79.7%.

The Prediction of Diabetes

“…[57] verify the presence of a relationship between diabetes and pedigree diabetes function through the Support Vector Machine. [58] it was estimated that in 2018 the number of diabetics in the world was equal to 451 million people and it is expected that this value in 2045 will reach an amount of 693 million people. [59] verify the presence of a positive relationship between Diabetes Pedigree Function and Diabetes through the use of a set of techniques and through the use of appropriate case studies.…”