CardioGuard: An AI-Powered Heart Health Analytics Platform

cardiovascular diseases remain one of the leading causes of mortality in the world, and thus, there is an urgency in the early diagnosis and proper intervention measures to cardiovascular diseases. Introduced in this paper, CardioGuard is a new AI-based heart health analytics product connecting the value of structural health monitoring with the value of digital transformation to offer cardiovascular risk analysis, lifestyle pattern analysis, Body Mass Index (BMI) calculation, and customized health recommendations. Continuous data acquisition, realtime analysis and predictive modeling are used to monitor physiological signals by CardioGuard, which makes interesting comparisons to the structural health monitoring systems designed to be useful in civil engineering and industrial applications. The system is designed with a React frontend user interface, a layer of the Node.js backend service, a Python FastAPI machine learning microservice, and a layer of a MongoDB database system. Multiple alternative ensemble learning models including: Random Forest, XGBoost, and LightGBM were trained systematically, validated, and tested based on cross-validation procedures. The model with the best prediction accuracy achieves a predictive accuracy of over 89% value and ROC-AUC value of approximately 92. By means of implementing the concept of structural monitoring to the physiological data streams, the system enables the proactive treatment based on the cardiovascular health management. By embedding the notion of structural monitoring on the traditional health checkup process, the system enables the implementation of the strategies of digital transformation, including secure cloud authentication, analytics dashboarding, tracking past health patterns, and AI-driven treatment recommendations.