Key result
The proposed ML-ELM model demonstrated superior predictive performance for forecasting heart attacks compared to alternative machine learning techniques, achieving an accuracy rate of 96.77%.
Why the study?
Predicting heart attacks is a significant concern contributing to global morbidity, and this research aims to enhance the accuracy of predicting heart disease through machine learning techniques.
The ML-ELM hybrid modeling strategy achieves high accuracy (96.77%) in predicting heart attacks based on clinical risk factors.
May enhance ML-based heart attack risk stratification; leaves open prospective clinical validation before practice adoption.
Predicting heart attacks stands as a significant concern contributing to global morbidity. Within clinical data analysis, cardiovascular disease emerges as a pivotal focus for forecasting, wherein Data Science and machine learning (ML) offer invaluable tools. These methodologies aid in predicting heart attacks by considering various risk factors Just like high blood pressure, increased cholesterol levels, irregular pulse rates, and diabetes, this research aims to enhance the accuracy of predicting heart disease through machine learning techniques.This study introduces a MLdriven approach, termed ML-ELM, dedicated to forecasting heart attacks by analysing diverse risk factors. The proposed ML-ELM model is compared with alternative Utilizing machine learning techniques like Support Vector Machines, Logistic Regression, Naïve Bayes, and XGBoost is a key aspect of this exploration into different approaches for predictive modeling., is part of the research strategy. The dataset utilized for heart disease symptoms is sourced from the UCI ML Repository. The outcomes reveal that our proposed ML-ELM model has demonstrated superior predictive performance among the ML techniques tested. ML models show notable efficiency in identifying heart attack symptoms, particularly with boosting algorithms. Accuracy assessments were employed to gauge the predictive ability, Our suggested model demonstrated an outstanding accuracy rate of 96.77%.
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A 2024 study studied Heart attacks. ML-ELM model vs. Support Vector Machines, Logistic Regression, Naïve Bayes, and XGBoost was evaluated on Predictive accuracy. The proposed ML-ELM model demonstrated superior predictive performance for forecasting heart attacks compared to alternative machine learning techniques, achieving an accuracy rate of 96.77%.
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