The Extra-Tree classifier combined with the Relief feature selection algorithm achieved a classification accuracy of 94.41% for the diagnosis of heart disease, outperforming other machine learning models.
Machine learning classification algorithms combined with feature selection techniques can provide an effective non-invasive computational model for heart disease diagnosis.
Absolute Event Rate: 94.41% vs 92.09%
p-value: p=<0.00001
Heart disease is a fatal human disease, rapidly increases globally in both developed and undeveloped countries and consequently, causes death. Normally, in this disease, the heart fails to supply a sufficient amount of blood to other parts of the body in order to accomplish their normal functionalities. Early and on-time diagnosing of this problem is very essential for preventing patients from more damage and saving their lives. Among the conventional invasive-based techniques, angiography is considered to be the most well-known technique for diagnosing heart problems but it has some limitations. On the other hand, the non-invasive based methods, like intelligent learning-based computational techniques are found more upright and effectual for the heart disease diagnosis. Here, an intelligent computational predictive system is introduced for the identification and diagnosis of cardiac disease. In this study, various machine learning classification algorithms are investigated. In order to remove irrelevant and noisy data from extracted feature space, four distinct feature selection algorithms are applied and the results of each feature selection algorithm along with classifiers are analyzed. Several performance metrics namely: accuracy, sensitivity, specificity, AUC, F1-score, MCC, and ROC curve are used to observe the effectiveness and strength of the developed model. The classification rates of the developed system are examined on both full and optimal feature spaces, consequently, the performance of the developed model is boosted in case of high variated optimal feature space. In addition, P-value and Chi-square are also computed for the ET classifier along with each feature selection technique. It is anticipated that the proposed system will be useful and helpful for the physician to diagnose heart disease accurately and effectively.
Muhammad et al. (Thu,) conducted a other in Heart disease (n=1,329). Extra-Tree (ET) classifier with Relief feature selection vs. Other machine learning algorithms and full feature space was evaluated on Classification accuracy (p=<0.00001). The Extra-Tree classifier combined with the Relief feature selection algorithm achieved a classification accuracy of 94.41% for the diagnosis of heart disease, outperforming other machine learning models.