Early Detection and Classification of Type-2 Diabetes Using Stratified k-Fold Validation

One of the diseases that spreads most rapidly in the globe is diabetes, that can lead to a number of serious complications, including as neuropathy, renal failure, diabetic retinopathy, and cardiovascular disease. Today, a large number of scientists and physicians have created machine learning Models. Even with the advancement of machine learning techniques, there are two major obstacles that researchers and physicians still need to overcome in order to create prediction models for type 2 diabetes. Stratified k-fold cross validation and a straightforward machine learning technique called logistic regression are used in this study to overcome all of these problems and increase the model's accuracy. Our model outperforms all other machine learning implemented model with higher accuarcy of 98.24% on Diabetes Dataset.