Explainable AI in Healthcare for Diabetes Diagnosis and Management - An Experimental study

This experimental research presents a comprehensive evaluation of a Pima Indians Diabetes Dataset using a Support Vector Machine (SVM) classifier for predictive analysis [1], combined with explainable artificial intelligence (XAI) techniques to interpret model decisions. The dataset, collected from actual field conditions, was preprocessed and analyzed to identify significant features influencing the prediction outcomes, following standard practices in real-world machine learning pipelines [2]. The SVM model was trained and optimized to achieve high classification performance, demonstrating its robustness in handling nonlinear patterns and complex data distributions [3]. To enhance transparency and interpretability—critical aspects in modern machine learning applications [4]—two XAI frameworks, SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-Agnostic Explanations), were applied. SHAP was used to quantify global and local feature contributions, enabling a deeper understanding of how input variables impact the model’s decision boundaries [5]. LIME provided localized, instance-level explanations that highlighted the key attributes driving individual predictions [6]. The combined use of SVM with SHAP and LIME not only improved model interpretability but also strengthened trust in the predictive logic, making the approach suitable for deployment in sensitive and decision-critical environments ( [7] ). The results demonstrate that integrating XAI methods with traditional machine learning models can significantly enhance model transparency without compromising predictive performance, aligning with recent findings in the field [8]. This research could provide a helping hand to those who want to understand and implement XAI for various domains.