A Comparative Study of Machine Learning Techniques for Early Prediction of Diabetes

TL;DR

This study evaluates various machine learning algorithms on the Pima Indians Diabetes dataset to identify the most effective method for early diabetes prediction, highlighting Neural Networks as the top performer with 78.57% accuracy.

Contribution

It provides a comparative analysis of multiple machine learning techniques for diabetes prediction using a standard dataset, identifying the most accurate approach.

Findings

Neural Network achieved 78.57% accuracy.

Random Forest achieved 76.30% accuracy.

Machine learning algorithms can effectively aid early diabetes detection.

Abstract

In many nations, diabetes is becoming a significant health problem, and early identification and control are crucial. Using machine learning algorithms to predict diabetes has yielded encouraging results. Using the Pima Indians Diabetes dataset, this study attempts to evaluate the efficacy of several machine-learning methods for diabetes prediction. The collection includes information on 768 patients, such as their ages, BMIs, and glucose levels. The techniques assessed are Logistic Regression, Decision Tree, Random Forest, k-Nearest Neighbors, Naive Bayes, Support Vector Machine, Gradient Boosting, and Neural Network. The findings indicate that the Neural Network algorithm performed the best, with an accuracy of 78.57 percent, followed by the Random Forest method, with an accuracy of 76.30 percent. The study implies that machine learning algorithms can aid diabetes prediction and be an efficient early detection tool.