Theophilus Bamise Ajala

A Comparative Analysis of Machine Learning Algorithms for the Early Prediction of Diabetes with an Evaluation of Class-Imbalance Handling

The study comes to the conclusion that headline accuracy is an unreliable guide in imbalanced medical prediction, that imbalance handling can change a model's practical usefulness, and that this benefit is strongly algorithm-dependent, meaning that the decision to resample should be based on the algorithm and the screening priorities rather than being applied consistently.