Key result
Multi-modal Random Forest AI achieves ~92% accuracy for CVD risk assessment.
Why the study?
Accessible practical screening tools for early cardiovascular and lifestyle disease risk identification in primary care remain very expensive, reliant on specialists, or both.
A Random Forest-based AI framework can accurately predict heart disease risk (91.8% accuracy, AUC 0.956) and integrate lifestyle analysis for rapid primary care screening.
More than 30% of worldwide deaths involve diseases caused by cardiovascular and lifestyle factors (WHO, 2023) As awareness of early risk identification advances, accessible practical screening tools for use in primary care continue to be either very expensive, reliant on specialists or both. In this paper, we propose a Multi-Modal AI-Based Health Intelligence Framework with an explicit focus on two interrelated concepts encapsulated in the form of two specialized individual modules: Disease Risk Assessment (DRA) module and Lifestyle Analysis (LSA) module. After systematic preprocessing and class-balancing, the DRA module trains LR, SVM, and RF on the Cleveland Heart Disease dataset (303 patients). The LSA module takes user-reported behavioral behaviors — BMI, physical activity, sleep, dietary quality, and stress — to calculate a composite Lifestyle Risk Index (LRI). Both modules are provided through a Streamlit web application that provides real time predictions with SHAP-based explanation. Amongst all the classifiers we evaluated, Random Forest performed best with a 91.8% accuracy, AUC-ROC = 0.956 It powers a sub 60 ms response time for the system and is deployable in the cloud.
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Singh et al. (2026) studied Heart Disease (n=303). Multi-Modal AI-Based Health Intelligence Framework (Random Forest) vs. Logistic Regression and Support Vector Machine models was evaluated on Model accuracy and AUC-ROC (AUC-ROC 0.956). A Random Forest-based Multi-Modal AI Health Intelligence Framework achieved 91.8% accuracy and an AUC-ROC of 0.956 for cardiovascular disease risk assessment.
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