Multimodal Machine Learning for Explainable Disease Risk Prediction and Clinical Decision

Early disease risk prediction plays a significant role in modern healthcare by supporting preventive care, timely diagnosis, and informed clinical decision-making. This paper presents a proposed multimodal machine learning framework for explainable disease risk prediction using clinical, demographic, lifestyle, and healthcare data. The framework incorporates data preprocessing, missing-value handling, feature selection, and classification techniques to identify individuals at elevated risk of developing diseases. Various machine learning and deep learning algorithms, including Logistic Regression, Support Vector Machine, Random Forest, Gradient Boosting, XGBoost, and neural networks, are considered for comparative predictive modelling. Explainable Artificial Intelligence techniques such as SHAP and LIME are incorporated to provide interpretable predictions by identifying the contribution of individual risk factors. The proposed architecture integrates data processing, predictive modelling, explainability, and a user-friendly interface to support healthcare professionals in making informed decisions. The study also addresses important challenges, including data quality, class imbalance, overfitting, model generalization, privacy, and clinical validation. Future enhancements include incorporating longitudinal electronic health records, wearable health data, federated learning, and advanced multimodal approaches. The proposed framework aims to improve transparency, reliability, and accessibility in AI-assisted disease risk assessment while supporting, rather than replacing, professional clinical judgment.