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Abstract
Heart disease remains one of the leading causes of mortality worldwide, creating an urgent need for early prediction and preventive strategies to reduce severe cardiac events. This study aims to develop a data-driven predictive model to estimate the likelihood of heart attack occurrence by analyzing major clinical and lifestyle-related risk factors. A publicly available dataset obtained from Kaggle was utilized, consisting of attributes such as age, gender, cholesterol levels, blood pressure, blood sugar levels, heart rate, smoking behavior, obesity, physical inactivity, and dietary habits, all of which contribute significantly to cardiovascular health. The data was processed and analyzed using statistical techniques to evaluate feature relevance, correlation patterns, and risk influence. Multiple machine learning algorithms were then implemented to build a predictive model capable of assessing heart attack risk with improved accuracy. The findings revealed that factors including cholesterol levels, blood pressure, lifestyle behaviors, and physiological health indicators were among the most influential contributors to cardiac risk. The predictive model demonstrated strong potential in risk estimation, supporting the significance of machine learning for medical prognosis. These results indicate that predictive analytics can significantly enhance early diagnosis, assist healthcare professionals in risk stratification, and support timely clinical decision-making. The study concludes that integrating machine learning-based prediction systems into healthcare practice can improve preventive care, enable personalized treatment planning, and contribute to reducing heart disease-related complications and mortality rates.
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