Application of Machine Learning for Early Detection and Prognosis of Chronic Diseases Using Multi-Modal Healthcare Data

Chronic diseases such as cardiovascular disorders, diabetes, cancer, and chronic respiratory illnesses represent a major global health burden. According to the World Health Organization (WHO), non-communicable diseases account for a significant percentage of global mortality. Early detection and accurate prognosis are critical for reducing mortality rates and healthcare expenditures. With the rapid digitization of healthcare systems, multi-modal data—including electronic health records (EHRs), medical imaging, genomic sequences, wearable sensor data, and clinical text—have become widely available. This paper presents a machine learning-based framework for early detection and prognosis of chronic diseases using multi-modal healthcare data. The study discusses data preprocessing, feature extraction, fusion strategies, model architectures, evaluation metrics, and challenges. Experimental observations indicate that multi-modal approaches significantly outperform unimodal models in predictive accuracy and robustness.