Performance Analysis of Machine Learning Techniques to Predict Diabetes Mellitus
This work addresses early prediction of diabetes for medical applications, but it is incremental as it applies existing methods to a common dataset.
The study compared four machine learning algorithms (SVM, Naive Bayes, KNN, and C4.5 Decision Tree) on adult population data to predict diabetes mellitus, finding that C4.5 Decision Tree achieved the highest accuracy.
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.