Diabetes Prediction at Early Stage Using Machine Learning

Abstract

The categorization of medical datasets using machine learning has piqued the interest of the academic community in recent years, despite the fact that it is a difficult undertaking. The use of a large number of machine learning algorithms to a collection of data aids in the completion of the processes. Many studies have been conducted in the past to predict disease using machine learning. However, there are several opportunities for improvement. The goal of this study is to show how pre-processing approaches, as well as traditional and ensemble classifiers, may be used to compare different machine learning-based models for diabetes prediction. The pre-processing procedures for processing the dataset include encoding categorical data, imputing missing values, handling imbalanced data, and scaling features are taken place in this exploration. Five classification techniques, including Support Vector Machine (SVM), Naive Bayes (NB), Logistic Regression (LR), Decision Tree (DT), and Extra Tree (ET), are used to classify the dataset using a 10-fold crossvalidation technique, as well as hyperparameter tuning in each classifier to assign the best parameters. Ensemble methods are used to improve the performance of traditional algorithms and prevent them from being over fitted and biased. The experimental study indicates diabetes predictions with a higher degree of accuracy, as well as evaluated the findings of other current studies, with 98.44% accuracy being the best.