Performance Analysis of Machine Learning Techniques to Predict Diabetes Mellitus

Abstract

Diabetes mellitus is a common disease of human body caused by a group of metabolic disorders where the sugar levels over a prolonged period is very high. It affects different organs of the human body which thus harm a large number of the body's system, in particular the blood veins and nerves.Early prediction in such disease can be controlled and save human life. To achieve the goal, this research work mainly explores various risk factors related to this disease using machine learning techniques. Machine learning techniques provide efficient result to extract knowledge by constructing predicting models from diagnostic medical datasets collected from the diabetic patients. Extracting knowledge from such data can be useful to predict diabetic patients. In this work, we employ four popular machine learning algorithms, namely Support Vector Machine (SVM), Naive Bayes (NB), K-Nearest Neighbor (KNN) and C4.5 Decision Tree, on adult population data to predict diabetic mellitus. Our experimental results show that C4.5 decision tree achieved higher accuracy compared to other machine learning techniques.

Using Machine Learning to Predict the Future Development of Disease

“…When it comes to utilising machine learning methods to predict diabetes or other diseases, different models have been proposed in recent years. The paper [12] used four separate machine learning algorithms to predict Diabetic Mellitus among the adult population, and the decision tree was found to provide higher accuracy. In [13], different decision tree classifiers are applied and evaluated based on their true positive rate and precision.…”

A Comparative Study on Risk Prediction Model of type 2 Diabetes based on Machine Learning Theory

“…Faruque [15] and Kandhasamy [16] found that the prediction effect of C4.5 decision tree model was signi cantly higher than that of SVM model. The results of this study showed that the prediction effects of SVM and CART models were similar, and the difference in AUC value was not statistically signi cant, and the prediction effects of the two models were slightly better than that of C4.5 decision tree model.…”

Design of a Web Application for the Detection of Diabetes using Machine Learning

“…Today, diabetes has become one of the most important and recognized diseases in the world, not only because of its terrible effects on the human body, but also because of its difficult previous diagnosis, according to medical data it is estimated that this disease will increase from 376 billion to 490 billion in 2030 [1], in another aspect, diabetes presents some symptoms, such as continuous urination, increased thirst, expanded craving and weight reduction [2]. Diabetes can be divided into several categories among the main ones are type 1, it is caused due to total insufficiency of insulin secretion and type 2 is caused due to the combination of immunity to insulin action and insulin deficiency [3].…”