Smart Artificial Intelligence System for Heart Disease Prediction
K Nagaiah
Dr. K Nagaiah, FST – Department of Electronics & Communications Engineering, THE ICFAI University Raipur, Raipur, (CG), India.
Manuscript received on 22 December 2023 | Revised Manuscript received on 28 December 2023 | Manuscript Accepted on 15 February 2024 | Manuscript published on 28 February 2024 | PP: 1-6 | Volume-13 Issue-3, February 2024 | Retrieval Number: 100.1/ijeat.C434613030224 | DOI: 10.35940/ijeat.C4346.13030224
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Heart disease plays a vital role in human life. Early detection of heart disease can save human lives, and it remains a leading cause of mortality worldwide, making early and accurate prediction of heart disease a critical task for improving patient outcomes. Machine learning has shown great promise in this area, with various models being developed to predict heart disease based on a range of clinical and demographic features. However, there is a growing need for more efficient machine learning models that can accurately predict heart disease while minimizing computational costs, particularly in resource-constrained settings. This research paper proposes an efficient machine learning model for heart disease prediction that combines feature selection, model optimization, and interpretability techniques to achieve accurate predictions with reduced computational complexity. The proposed model utilises a dataset of clinical and demographic features, including age, sex, blood pressure, cholesterol levels, and other relevant risk factors, to train a machine learning model using a large, real-world dataset. The proposed efficient machine learning model is evaluated on benchmark datasets and compared with other state-of-the-art models in terms of precision, Accuracy, Recall, and F1-score. The results demonstrate that the model achieves superior prediction performance compared to existing models. Proposed method accuracy increased by 4.8%
Keywords: Heart Disease, Machine Learning, SVM, Decision Tree, Logistic Regression, Accuracy, Sensitivity
Scope of the Article: Machine Learning