An Explainable Artificial Intelligence Approach for Early Disease Prediction and Risk Assessment Using Healthcare Big Data

Abstract

The swift development of healthcare big data produced by electronic health records, wearable sensors, laboratory reports, and medical imaging systems has created strong prospects in predicting diseases earlier. Nonetheless, the current artificial intelligence designs tend to have low interpretability, low clinical trust, and low performance when applied to heterogeneous and high-dimensional healthcare data. Specifically, the black-box methods of deep learning do not offer clear explanations of disease predictions and, thus, cannot be adopted in real-life clinical settings. To overcome these issues, this paper suggests an Explainable Artificial Intelligence (XAI)-based method of early disease prediction and smart risk assessment with the help of healthcare big data analytics. To enhance transparency, interpretability, and clinician confidence, the proposed system will combine state-of-the-art machine learning models with the explainability algorithms of SHAP and LIME. Hybrid predictive architecture that combines XGBoost and deep neural networks are used to analyze large patient data and categorize patients as low-, medium- and high-risk. Experimental analysis with benchmark healthcare data reveals that the recommended framework obtains a 96.2% prediction accuracy, a 95.1% precision, a 94.6% recall, and a 95.8% F1-score, which is superior to traditional machine learning techniques. Also, the explainability layer enhances greatly clinical interpretability and aids sound decision-making in proactive and personalized healthcare management.