Predictive Analytics for Early Detection of Chronic Diseases Using Multimodal Healthcare Data

Abstract

Timely intervention through early diagnosis of chronic conditions like diabetes, blood pressure and cancer relapse is essential to reduce the end outcomes of patients. The recent development in predictive analytics and machine learning has made it possible to extract meaningful patterns in complex healthcare data. The existing studies are however based mostly on single modality data which limits the predictability and the generality of their models. The proposed research is a multimodal predictive analytics model that incorporates various sources of healthcare data such as electronic health records (EHRs), laboratory tests, and medical imaging and wearable sensor data to help improve the process of early disease detection. There were different machine learning models created, such as the random forest and XGBoost or the deep neural networks and these have been tested on their publicly available datasets. Techniques of data preprocessing and fusion were used to deal with heterogeneity to enhance model robustness. The experimental findings prove that multimodal models are much better than unimodal models since they are more accurate and better calibrated to make early predictions of diabetes, hypertension and cancer relapse. In addition, interpretation analysis based on SHAP values offered clinical value on the most important risk factors affecting model decisions. The suggested scheme shows the opportunities of multimodal machine learning in identifying chronic disease at an early and accurate stage of early detection, and opens the path to data driven precision medicine and proactive healthcare management.