An Optimized Stacked Support Vector Machines Based Expert System for the Effective Prediction of Heart Failure

Ali, Liaqat; Niamat, Awais; Khan, Javed Ali; Golilarz, Noorbakhsh Amiri; Xiong, Xingzhong; Noor, Adeeb; Nour, Redhwan; Bukhari, Syed

doi:10.1109/access.2019.2909969

Cited by 209 publications

(100 citation statements)

References 19 publications

(17 reference statements)

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

Section: Discussionmentioning

confidence: 99%

“…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…”

Section: Declarationsmentioning

confidence: 99%

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A hybrid cost-sensitive ensemble for heart disease prediction

Zhang

2020

Preprint

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Background: Heart disease is the primary cause of morbidity and mortality in the world. It includes numerous problems and symptoms. The diagnosis of heart disease is difficult because there are too many factors to analyze. What’s more, the misclassification cost could be very high. Methods: A cost-sensitive ensemble model was proposed to improve the efficiency of diagnosis and reduce the misclassification cost. The proposed model contains five heterogeneous classifiers: random forest, logistic regression, support vector machine, extreme learning machine and k-nearest neighbor. T-test was used to investigate if the performance of the ensemble model was better than individual classifiers and the contribution of Relief algorithm. Results: The best performance was achieved by the proposed model according to ten-fold cross validation. The statistical tests demonstrated that the performance of the proposed model was significantly superior to individual classifiers, and the efficiency of classification was distinctively improved by Relief algorithm. Conclusions: The proposed ensemble model gained significantly better results compared with individual classifiers and previous studies, which implies that it can be used as a promising alternative tool in medical decision making for heart disease diagnosis.

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

Section: Discussionmentioning

confidence: 99%

Section: Declarationsmentioning

confidence: 99%

A hybrid cost-sensitive ensemble for heart disease prediction

Zhang

2020

Preprint

View full text Add to dashboard Cite

show abstract

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

Section: Discussionmentioning

confidence: 99%

A hybrid cost-sensitive ensemble for heart disease prediction

Zhang²

2020

Preprint

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Background: Heart disease is the primary cause of morbidity and mortality in the world. It includes numerous problems and symptoms. The diagnosis of heart disease is difficult because there are too many factors to analyze. What's more, the misclassification cost could be very high. Methods: A cost-sensitive ensemble method was proposed to improve the efficiency of diagnosis and reduce the misclassification cost. The proposed method contains five heterogeneous classifiers: random forest, logistic regression, support vector machine, extreme learning machine and k-nearest neighbor. T-test was used to investigate if the performance of the ensemble was better than individual classifiers and the contribution of Relief algorithm. Results: The best performance was achieved by the proposed method according to ten-fold cross validation. The statistical tests demonstrated that the performance of the proposed ensemble was significantly superior to individual classifiers, and the efficiency of classification was distinctively improved by Relief algorithm. Conclusions: The proposed ensemble gained significantly better results compared with individual classifiers and previous studies, which implies that it can be used as a promising alternative tool in medical decision making for heart disease diagnosis.

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“…Figures 3-6 show the receiver operating characteristic (ROC) curve for the three failure prediction models that used different numbers of features. The ROC curve is a performance measure of the prediction model and presents the relationship between the true positive rate (TPR) and the false positive rate (FPR) [34]. The TPR and FPR values were calculated using Equations (10) and (11), respectively.…”

Section: Implementation and Performance Evaluationmentioning

confidence: 99%

Failure Prediction Model Using Iterative Feature Selection for Industrial Internet of Things

Kwon

Kim

2020

Symmetry

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This paper presents a failure prediction model using iterative feature selection, which aims to accurately predict the failure occurrences in industrial Internet of Things (IIoT) environments. In general, vast amounts of data are collected from various sensors in an IIoT environment, and they are analyzed to prevent failures by predicting their occurrence. However, the collected data may include data irrelevant to failures and thereby decrease the prediction accuracy. To address this problem, we propose a failure prediction model using iterative feature selection. To build the model, the relevancy between each feature (i.e., each sensor) and the failure was analyzed using the random forest algorithm, to obtain the importance of the features. Then, feature selection and model building were conducted iteratively. In each iteration, a new feature was selected considering the importance and added to the selected feature set. The failure prediction model was built for each iteration via the support vector machine (SVM). Finally, the failure prediction model having the highest prediction accuracy was selected. The experimental implementation was conducted using open-source R. The results showed that the proposed failure prediction model achieved high prediction accuracy.

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An Optimized Stacked Support Vector Machines Based Expert System for the Effective Prediction of Heart Failure

Cited by 209 publications

References 19 publications

A hybrid cost-sensitive ensemble for heart disease prediction

A hybrid cost-sensitive ensemble for heart disease prediction

A hybrid cost-sensitive ensemble for heart disease prediction

Failure Prediction Model Using Iterative Feature Selection for Industrial Internet of Things

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