Predictive modeling for early detection of diabetes using machine learning techniques.

Abstract

Diabetes is a prevalent chronic disease with significant health implications worldwide. It is usually prolonged in a patient for their entire vitality. Early detection and intervention are vital for successfully managing and preventing any complications. Diabetes can lead to complications if not recognized and diagnosed early enough. In this dissertation, I will be talking about how machine-learning methods are crucial for predictive modeling. Aimed at early detection of diabetes. These models will be based on different factors, including demographic, clinical, and lifestyle, among others, with large datasets being used to come up with them. Therefore, the author prefers using machine learning methods such as SVM, KNN, ANN, Naive Bayes, logistic regression, XGB Classifier, and Decision Tree. The results are evaluated using performance measures including recall, accuracy, precision, and the F-measure, which are computed from the confusion matrix. I designed a predictive model to identify whether a patient will develop diabetes, utilizing specific diagnosis measurements in the dataset. This project tries to find a way to improve healthcare outcomes by enabling early intervention and enhanced disease management.