New Machine Learning Model Achieves Over 95% Accuracy in Predicting Heart Disease
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Researchers developed a new machine learning model (ML-HDPM) to predict heart disease with over 95% accuracy using multiple datasets and classification methods.
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The model selects important features, balances uneven data, and uses deep learning and optimization techniques to improve prediction.
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In tests, ML-HDPM achieved 96% accuracy and 95% precision for detecting heart disease, outperforming other models.
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Key strengths are combining feature selection, data balancing, and machine learning for reliable disease forecasting to aid clinical decisions.
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Further research can refine the model and evaluate real-world usage, but it shows promise to improve cardiovascular disease diagnosis.