Due to its intricate dependencies on numerous factors, diabetes diagnosis is a very difficult task in the early stages. In order to assist medical experts in the demonstration method, it is necessary to establish restorative symptomatic emotionally supportive networks. Neural system functions have been successfully linked to the diagnosis of many medical conditions. Gradient boosting machine learning is used in this thesis to train the diabetes diagnosis and classify diabetic patients into two groups based on their class values. To attain an accuracy of 81.95% in the suggested strategy, we employed an ensemble of gradient boosting techniques.For diabetic disease dataset, the majority vote-based model, which includes Naïve Bayes, Decision Tree, and Support Vector Machine classifiers, achieved an accuracy of 76.56%, sensitivity of 79.16%, and specificity of 77.476%. ...
Authors: Prachi Patel, Manoj Yadav.