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MACHINE LEARNING-BASED APPROACH FOR PREDICTING HEART DISEASE
Charan Jammula Divya Bagi Nagaraju Yadav Konda
M. Anand
Geethanjali College of Engineering and Technology Hyderabad India
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Abstract
Heart disease is a significant public health issue, accounting for a large percentage of global deaths and rising healthcare expenditures. This research seeks to enhance early heart disease detection using sophisticated machine learning techniques with the Random Forest classifier. A dataset of 4,240 individuals was utilized, containing a mix of clinical data and personal background details—age, sex, cholesterol level, blood pressure, smoking status, and body mass index—we adopted a systematic methodology involving data preprocessing, missing value treatment, feature scaling, and hyperparameter optimization. With cross-validation and careful testing, the model was optimized to deliver the optimal performance.
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Jammula, Charan, et al.. "Machine Learning-Based Approach for Predicting Heart Disease." International Journal of Creative and Open Research in Engineering and Management, vol. , no. , , pp. . doi:https://doi.org/10.55041/ijcope.v2i2.158.
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