Early Detection of Cardiovascular Disease Risk Using Multi-Parameter Biomarker Analysis and Machine Learning: A Prospective Cohort Study

Abstract

ABSTRACT Background Cardiovascular disease (CVD) remains the leading cause of mortality globally, with many events occurring in individuals without prior diagnosed conditions. Early risk stratification using accessible biomarkers could enable timely intervention and reduce adverse outcomes. Objective To evaluate the performance of a machine learning-based risk prediction model utilizing routine physiological parameters for early detection of cardiovascular disease risk 4-6 weeks prior to clinical manifestation. Methods A prospective cohort study was conducted from January 2024 to January 2025 involving 500 employees (300 males, 200 females; age range 35-50 years) recruited through ProMed Solutions Pvt. Ltd., Pakistan. Participants with no prior diagnosed cardiac conditions underwent weekly screenings measuring body mass index (BMI), blood pressure (systolic and diastolic), heart rate, single-lead electrocardiogram (ECG), and random blood glucose. A supervised machine learning algorithm generated cardiovascular risk scores. Monthly comprehensive cardiac evaluations including complete blood work, 12-lead ECG, echocardiography, and ultrasound imaging were performed by PAF Hospital, Islamabad, serving as clinical validation endpoints. Results Over 26,000 individual screening sessions were completed with 98.4% adherence. The ML model achieved 96.0% overall accuracy (480/500), 71.05% sensitivity (27/38 true positives), and 98.05% specificity (453/462 true negatives). The model correctly identified 27 of 38 individuals who developed early-stage CVD during the study period (true positives), with 11 false negatives. Among 462 individuals without CVD development, 453 were correctly classified (true negatives) with 9 false positives. Positive predictive value was 75.0% (27/36) and negative predictive value was 97.6% (453/464). Male participants with BMI 28-30 kg/m², pulse pressure 60-74 mmHg, ECG showing ventricular ectopy or ST-segment abnormalities, and random glucose 156-164 mg/dL demonstrated 81.5% probability of early-stage CVD detection confirmed through comprehensive clinical investigation. Conclusions Integration of routine physiological parameters with machine learning algorithms demonstrates high specificity and acceptable sensitivity for early cardiovascular risk detection in asymptomatic working-age adults. The model’s high negative predictive value suggests utility for population-level screening, though modest sensitivity indicates complementary clinical assessment remains essential for comprehensive risk stratification.

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