XGBClassifier and Explainable AI for Disease Classification from Patient Symptom and Demographic Data with Late Fusion

The accurate identification of diseases based on patient symptoms and demographic data is a critical area of healthcare research, with the potential to significantly improve patient outcomes. In this study, we employed machine learning algorithms, specifically the XGBClassifier, to classify diseases based on a dataset containing patient symptoms and demographic information such as age, gender, fever, cough, fatigue, and other health indicators. To ensure model interpretability and transparency, we incorporated Explainable AI (XAI) techniques like SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations), which allow us to understand and interpret the feature contributions to the model’s predictions. The results demonstrated that the XGBClassifier achieved an accuracy of 81.42%, outperforming other machine learning models tested. This study emphasizes the importance of combining XAI with machine learning for disease classification, offering greater transparency in the decision-making process, which is vital in healthcare settings.