A Comparative Study of Machine Learning Techniques for Early Prediction of Diabetes
This work addresses diabetes prediction for healthcare applications, but it is incremental as it applies existing methods to a standard dataset without novel methodological contributions.
This study tackled the problem of early diabetes prediction by comparing multiple machine learning techniques on the Pima Indians Diabetes dataset, finding that a Neural Network achieved the highest accuracy of 78.57%, followed by Random Forest at 76.30%.
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.