Predictive Analytics in Chronic Disease Management Using AI to Enhance Cardiovascular Health and Patient Record Outcomes
DOI:
https://doi.org/10.62497/irjai.117Keywords:
Predictive analytics, artificial intelligence, AI, disease management, clinical decision support, healthcare outcomesAbstract
Introduction: The integration of AI and predictive analytics into clinical workflows is transforming chronic disease management by enabling early risk detection, personalized treatment, and better resource use, especially in cardiovascular care. This study evaluated the clinical impact, predictive accuracy, and practical feasibility of AI-driven analytics for improving cardiovascular outcomes and optimizing patient records in a tertiary care setting.
Methodology: A prospective cohort study was conducted at KUST, Kohat, and PIMS, Islamabad, from January to December 2023, enrolling 290 adults with cardiovascular disease. Data from electronic health records and structured interviews informed supervised machine learning models (logistic regression and random forest), validated via cross-validation. Deployed as a real-time Streamlit app, the models were compared to the Framingham Risk Score. Primary outcomes were cardiovascular event prediction, with secondary analyses on treatment recommendations, patient satisfaction, and healthcare utilization pre- and post-AI implementation.
Results: The mean patient age was 58.4 years (SD ± 10.2), with a male predominance (54.48%). Common comorbidities included hypertension (70.34%), hyperlipidemia (42.76%), and diabetes (36.90%). The random forest model outperformed traditional risk scoring, achieving 81.57% accuracy, 85.31% sensitivity, and 78.96% specificity. Implementation of the AI-driven platform led to a reduction in cardiovascular events, including myocardial infarctions (from 28 to 19 cases) and strokes (from 18 to 9 cases). Post-AI, significant improvements were observed in lifestyle modifications (+13.11%), intervention referrals (+24.44%), and surgical procedures (+33.33%). Additionally, hospital admissions and emergency visits declined by 12.5% and 26.88%, respectively. Patient satisfaction was notably high, with 85.17% reporting ease of use and 80.69% expressing trust in AI-guided recommendations.
Conclusion: The deployment of AI-powered predictive analytics in cardiovascular care demonstrated significant improvements in risk prediction accuracy, patient outcomes, and healthcare delivery efficiency. These findings support the integration of AI as a viable, patient-centered tool in chronic disease management, offering a path toward more proactive, precise, and data-driven clinical interventions.
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Copyright (c) 2025 Dr. Haris Khan, Dr. Azaz Alam, Dr. Hesna Bouid, Dr. Hina Kamawal, Muhammad Hozaifa (Author)
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