Prediction of Diabetes at Early Stage using Interpretable Machine Learning

Diabetes, for a long period of time, was misjudged as a trivial concerned disease but has now risen to become one of the fastest-growing chronic diseases, affecting around 463 million people worldwide. In most cases of diabetes, patients are unaware of the disease due to the moderately long asymptomatic stage, and the prevention process becomes complicated with the delay since most of the cases of diabetes remain undiagnosed. Therefore, the initial stage diagnosis of diabetes is an important factor in order to enable clinically meaningful outcomes. To determine the likelihood of having diabetes, our study utilizes a dataset that includes both newly diabetic and would-be diabetic patients and employs five different machine-learning algorithms. Results indicate that Random Forest is the best model with an overall accuracy of around 99%. We also use an interpretable machine learning technique to determine the correlation between the response variable and the predictors.