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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 (CVD) are one of the leading causes of death globally.
According to the Pan American Health Organization (PAHO), CVD accounts for approximately 30% of all deaths in Latin America [1]. Despite well-known risk factors such as hypertension, dyslipidemia, and lifestyle habits, early detection and intervention remain challenges in traditional medicine.
This leads to late interventions and less effective management of high-risk patients [2].
Implementing AI predictive models to identify individuals at risk of cardiovascular diseases offers an opportunity for early and personalized intervention.
The model would use data such as age, sex, blood pressure, cholesterol levels, and lifestyle factors (physical activity, diet, smoking) to predict the likelihood of a patient developing CVD in the coming years.
These predictions would enable doctors to tailor interventions to each patient's specific needs, improving treatment effectiveness and reducing long-term complications.
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
Cardiovascular diseases (CVD) are one of the leading causes of death globally.
According to the Pan American Health Organization (PAHO), CVD accounts for approximately 30% of all deaths in Latin America [1]. Despite well-known risk factors such as hypertension, dyslipidemia, and lifestyle habits, early detection and intervention remain challenges in traditional medicine.
This leads to late interventions and less effective management of high-risk patients [2].
Implementing AI predictive models to identify individuals at risk of cardiovascular diseases offers an opportunity for early and personalized intervention.
The model would use data such as age, sex, blood pressure, cholesterol levels, and lifestyle factors (physical activity, diet, smoking) to predict the likelihood of a patient developing CVD in the coming years.
These predictions would enable doctors to tailor interventions to each patient's specific needs, improving treatment effectiveness and reducing long-term complications.
The cost of CVD is substantial. In Latin America, the cost of CVD in terms of healthcare and lost productivity is approximately 8.4 trillion dollars annually [1].
Early detection could reduce this cost, as prevention and early treatment of cardiovascular diseases lower hospitalizations and severe complications.
A study in the United States suggests that using AI tools to predict cardiovascular risk could reduce healthcare costs by 20% through improved management of high-risk patients [5].
Adopting an AI-based predictive model has the potential to save lives and improve the quality of life for millions of people by enabling earlier detection and more personalized treatment. It could also significantly reduce the costs associated with CVD, enhancing healthcare system efficiency. For instance, AI adoption in cardiology in Latin America could reduce hospitalizations for CVD by 15% (Sanofi, 2023). This approach could transform how CVD is managed, shifting from a reactive to a proactive and preventive model.
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
