Diabetes prediction system using ml & dl techniques

Abstract

Diabetes nowadays is a familiar and long-term disease. If a prediction is made early better treatment can be provided. The data pre-processing approach is extremely useful in predicting the disease at an early stage. “A number of tools are used in determining significant characteristics such as selection, prediction, and association rule mining for diabetes. The principal component analysis method was used to select significant attributes. Our judgments denote a firm association of diabetes with body mass indicator (BMI) and with glucose degree. The study implemented logistic regression, decision trees, and ANN techniques to process Pima Indian diabetes datasets and predict whether people at risk have diabetes. It was analysed that random forest had the best accuracy of 80.52 %. Out of 500 negative records 268 positive records our model correctly analysed 403 records 216 records respectively.