Early Diabetes Prediction Based on Machine Learning Approach

Abstract

The number of diabetic patients around the world is increasing alarmingly day by day. It has now become a threat to our human society. This disease is usually caused by eating heavy sugary foods and not following a proper diet. However, nowadays machine learning algorithms can be used to easily and accurately predict for diabetics by checking and sorting out different types of symptoms. This can greatly reduce our mortality rate and make us more aware of diabetes. The purpose of my work is to make patients aware of and predict diabetes in advance using machine learning algorithms. I have used three algorithms for this task- Logistic Regression, Gaussian Naive Bayes, Random Forest. The overall performance of the three algorithms is evaluated in exceptional steps which includes accuracy, precision, F1 score, ROC accuracy, Recall, Standard deviation and KFold mean accuracy. Analyzing the all algorithms, it is seen that the random forest algorithm gave the best result of 86%.