Key result
Hybrid machine learning model predicts heart disease with ~91% accuracy.
Why the study?
Early prediction of heart disease and selection of the most important influential parameters are vital to reduce its incidence, with advanced AI methods offering improved accuracy over traditional approaches.
Does a hybrid machine learning model combining multiple architectures and Importance-Performance Analysis accurately predict heart disease?
Does a hybrid machine learning model combining multiple architectures and Importance-Performance Analysis accurately predict heart disease?
A hybrid machine learning model incorporating importance-performance analysis achieved 91% accuracy in predicting heart disease across multiple standard datasets.
May support AI-driven parameter selection for heart disease prediction; leaves open need for prospective validation before clinical adoption.
Heart disease has been identified as one of the most common diseases among people worldwide, and early prediction of this disease can significantly help reduce its incidence. Selecting the most important influential parameters to reduce the risk of this disease is another vital factor in this field. Over time, with the advancement of science and technology, advanced methods, including artificial intelligence algorithms, have been an appropriate alternative to some of the old statistical and laboratory methods. These artificial intelligence algorithms, which include machine learning and deep learning architectures, can assist researchers in predicting heart diseases and identifying influential factors with high accuracy. For this purpose, in this study, which aims to predict heart disease and identify the most important influencing factors, a hybrid model that is the result of combining six machine learning and deep learning architectures, including Logistic Regression, Quadratic Discriminant Analysis (QDA), ANN, XGBoost, Naïve Bayes, and Gradient Boosting, was introduced on a combination of four datasets: Cleveland, Hungary, Switzerland, and VA Long Beach. The most important features were selected using the statistical feature analysis method, and the most important influencing variables were selected using the Importance-Performance Analysis (IPA) technique. The results of this study show that the proposed hybrid model, using the most important features, achieved the highest evaluation parameters, including an accuracy of 91%, recall of 92%, precision of 88%, and F1-score equal to 90%, and predicted healthy people from unhealthy people.
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Najafabadi et al. (2026) studied Heart disease. Hybrid machine learning and deep learning model (using Importance-Performance Analysis) was evaluated on Heart disease prediction (discriminating healthy from unhealthy individuals). A hybrid machine learning model using Importance-Performance Analysis for feature selection predicted heart disease with 91% accuracy, 92% recall, 88% precision, and an F1-score of 90%.
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