AI-Driven Lifestyle Risk Modeling for Early Cardiovascular Health Prediction

Cardiovascular disease remains one of the leading causes of mortality worldwide. Traditional diagnostic systems often rely on delayed clinical indicators, limiting the ability to intervene during early stages of health decline. Recent advancements in artificial intelligence and predictive analytics have introduced opportunities for continuous health risk assessment using behavioral and lifestyle-based data.

This research explores the development of a scalable AI-powered model capable of analyzing multidimensional health indicators including sleep quality, activity levels, nutrition patterns, stress exposure, and biometric trends. The proposed framework aims to improve early detection and support proactive healthcare strategies

The study utilized a hybrid machine learning architecture combining supervised classification and statistical risk scoring techniques. Synthetic and anonymized datasets representing cardiovascular-related lifestyle indicators were processed and normalized before training.

The model evaluated variables including:

• Daily physical activity
• Sleep duration and consistency
• Nutritional balance
• Heart rate trends
• Stress exposure levels
• Smoking and alcohol frequency
• BMI and age-related metrics

Data preprocessing included feature scaling, outlier reduction, and categorical encoding. Multiple predictive algorithms were benchmarked, including Random Forest, XGBoost, and Logistic Regression models. Performance evaluation was conducted using precision, recall, F1-score, and ROC-AUC metrics.

The AI-driven framework demonstrated strong predictive capability in identifying elevated cardiovascular risk categories. Ensemble-based models outperformed baseline statistical methods with improved classification stability and reduced false positives.

Key findings include:

• Increased prediction accuracy for high-risk individuals
• Strong correlation between sleep irregularity and cardiovascular stress markers
• Improved early-stage risk identification using combined lifestyle indicators
• Scalable deployment potential for digital health platforms

The optimized model achieved high consistency across validation datasets and maintained robust performance under varying demographic conditions.

This research demonstrates the feasibility of AI-powered lifestyle risk modeling as a scalable preventive healthcare solution. By integrating non-invasive behavioral indicators with machine learning systems, the framework enables earlier identification of cardiovascular health risks and supports proactive intervention strategies.

Future work will focus on expanding multimodal health data integration, real-time monitoring capabilities, and personalized risk adaptation systems for broader healthcare applications within the Aevon ecosystem.