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CVDs cause millions of deaths and high costs; AI can identify high-risk patients, improve care, and reduce expenses.
Cardiovascular diseases (CVDs) encompass a broad spectrum of disorders affecting the heart and blood vessels, including coronary artery disease, stroke, heart failure, and peripheral artery disease. These conditions collectively stand as the leading cause of mortality globally, surpassing deaths from all cancers and chronic lower respiratory diseases combined (1)(2). In the United States alone, CVDs annually claim approximately 19 million lives. The economic impact is substantial, with these diseases costing the U.S. economy an estimated $378 billion in 2018, encompassing direct healthcare costs, productivity losses, and premature death (3)(4). The prevalence of CVDs among American adults is staggering, with an estimated 126.9 million affected individuals. Key risk factors such as hypertension, high cholesterol, diabetes, obesity, physical inactivity, smoking, and unhealthy dietary habits significantly contribute to the onset and progression of CVDs (5)(6). Moreover, disparities in healthcare access and socioeconomic factors exacerbate the burden, disproportionately affecting underserved populations and minority communities (7).
CardioRiskAI: An AI model that predicts cardiovascular disease risk using patient data, integrating age, sex, blood pressure, cholesterol levels and lifestyle factors to early identify people at risk and tailor interventions.
A heart disease dataset was used, from a multi-specialty hospital in India and available on Kaggle, which covers essential features for research and early detection of heart diseases. With data from 1000 subjects and 12 key attributes, including age, sex, resting blood pressure, serum cholesterol levels and various clinical indicators.
