A Feature-Driven Decision Support System for Heart Failure Prediction Based on χ2 Statistical Model and Gaussian Naive Bayes

Abstract

Heart failure (HF) is considered a deadliest disease worldwide. Therefore, different intelligent medical decision support systems have been widely proposed for detection of HF in literature. However, low rate of accuracies achieved on the HF data is a major problem in these decision support systems. To improve the prediction accuracy, we have developed a feature-driven decision support system consisting of two main stages. In the first stage, χ2 statistical model is used to rank the commonly used 13 HF features. Based on the χ2 test score, an optimal subset of features is searched using forward best-first search strategy. In the second stage, Gaussian Naive Bayes (GNB) classifier is used as a predictive model. The performance of the newly proposed method (χ2-GNB) is evaluated by using an online heart disease database of 297 subjects. Experimental results show that our proposed method could achieve a prediction accuracy of 93.33%. The developed method (i.e., χ2-GNB) improves the HF prediction performance of GNB model by 3.33%. Moreover, the newly proposed method also shows better performance than the available methods in literature that achieved accuracies in the range of 57.85–92.22%.

A hybrid cost-sensitive ensemble for heart disease prediction

“…Overall, the proposed model has following advantages compared with the stateof-the-art methods [48][49][50][51] :…”

“…Ethics approval and consent to participate Not applicable. 1 the result of the test, h = 1 indicates that the null hypothesis can be rejected at the 5% level 2 the probability of observing the given result by chance if the null hypothesis is true [56] Hybrid neural network 93 78.5 Shah et al [57] PPCA * + SVM 75 90.57 Marian and Filip [58] Fuzzy rule-based classification 84.70 92.90 Ali et al [48] Gaussian Naive Bayes classifier 87.80 97.95 Ali et al [49] Deep neural network 85.36 100 Ali et al [50] Hybrid SVM 82.92 100 Ali et al [51] Deep belief network 96.03 93.15 Arabasadi et al [59] Hybrid neural network-genetic algorithm 88 91 Mokeddem and Ahmed [41] Fuzzy…”

A hybrid cost-sensitive ensemble for heart disease prediction

“…Overall, the proposed model has following advantages compared with the stateof-the-art methods [50][51][52][53] :…”

“…The average performance on ten folds.The best result is bolded. [58] Hybrid neural network 93 78.5 Shah et al [59] PPCA 1 + SVM 75 90.57 Marian and Filip [60] Fuzzy rule-based classification 84.70 92.90 Ali et al [50] Gaussian Naive Bayes classifier 87.80 97.95 Ali et al [51] Deep neural network 85.36 100 Ali et al [52] Hybrid SVM 82.92 100 Ali et al [53] Deep belief network 96.03 93.15 Arabasadi et al [61] Hybrid neural network-genetic algorithm 88 91 Mokeddem and Ahmed [41] Fuzzy The values listed in the table represent the average performance on ten folds.…”

A hybrid cost-sensitive ensemble for heart disease prediction

“…Compared with the state-of-the-art methods [47][48][49][50], the proposed model has certain advantages: (1) It is easily understood by less experienced clinical physicians, which makes it easier to implement. (2) Considering different kinds of misclassification cost makes the proposed model closer to reality.…”