HEART DISEASE PREDICTION USING MACHINE LEARNING WITH PYTHON
DOI:
https://doi.org/10.63503/c.acset.2025.23Keywords:
Heart Disease Prediction, Machine Learning, Ensemble Models, Feature Engineering, Python, Healthcare AnalyticsAbstract
In every year, heart disease is one of the major causes of death for millions of people all over the world. Therefore, diagnosis and risk prediction play a crucial role in reducing its impact on human life. This research presents a data-driven approach that helps in predicting heart diseases using multiple machine learning algorithms and techniques, implemented in Python. The study focuses on effective and accurate data preprocessing, exploratory analysis, feature selection, and model optimization to achieve reliable and accurate prediction of heart disease, using models such as Logistic Regression, Random Forest, and Gradient Boosting.
We have compared all these models based on their performance and accuracy metrics. The research findings show that ensemble-based models exhibit superior accuracy and robustness compared to traditional classifiers. The proposed framework highlights the potential of machine learning as a clinical decision-support tool, offering a cost-effective and accessible method for early detection of cardiovascular risk.
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