Enhancing diabetes prediction: integrating machine learning with explainable artificial intelligence

Abstract

Early detection of diabetes is critical in preventing disease progression and improving patient outcomes. This work combines Explainable Artificial Intelligence (XAI) and machine learning to enhance understanding and prediction of diabetes using the Pima Indian Diabetes Dataset. The machine learning models used in this study are Random Forest, Logistic regression and Gradient Boosting, which resulted in the best accuracy of 93.2%. Some of the pre-processing steps taken were handling of missing data, normalization, feature scaling and Synthetic Minority Over-sampling Technique (SMOTE) for handling class imbalance. Use of SHAP and LIME XAI methods has proven that glucose, BMI and insulin are the most crucial features when it comes to prediction. These techniques further enhance the trust of the clinicians and stakeholders by improving the understanding of how the features contribute to individual predictions, which enhances the model prediction as a whole. The findings prove that there is indeed a marked improvement in the understanding of the machine learning models and their predictions with no compromise on performance. This study highlights the benefits of using XAI in machine learning so that there is accuracy and ease of interpretation with immense power within the developed models.