A Hybrid Classification System for Heart Disease Diagnosis Based on the RFRS Method

Liu, Xiao; Wang, Xiaoli; Su, Qiang; Zhang, Mo; Yu, Zhu; Wang, Qiugen; Wang, Qian

doi:10.1155/2017/8272091

Cited by 130 publications

(60 citation statements)

References 48 publications

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“…X. Liu et al [15] presented a study to assist in the diagnosis of heart disease using a hybrid classification system based on the ReliefF and Rough Set (RFRS) method. The proposed system consists of two subsystems: the RFRS feature selection system and a classification system with an overall classifier.…”

Section: Related Workmentioning

confidence: 99%

Heart Disease Prediction and Classification Using Machine Learning Algorithms Optimized by Particle Swarm Optimization and Ant Colony Optimization

Khourdifi¹,

Bahaj²

2019

IJIES

211

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The prediction of heart disease is one of the areas where machine learning can be implemented. Optimization algorithms have the advantage of dealing with complex non-linear problems with a good flexibility and adaptability. In this paper, we exploited the Fast Correlation-Based Feature Selection (FCBF) method to filter redundant features in order to improve the quality of heart disease classification. Then, we perform a classification based on different classification algorithms such as K-Nearest Neighbour, Support Vector Machine, Naïve Bayes, Random Forest and a Multilayer Perception | Artificial Neural Network optimized by Particle Swarm Optimization (PSO) combined with Ant Colony Optimization (ACO) approaches. The proposed mixed approach is applied to heart disease dataset; the results demonstrate the efficacy and robustness of the proposed hybrid method in processing various types of data for heart disease classification. Therefore, this study examines the different machine learning algorithms and compares the results using different performance measures, i.e. accuracy, precision, recall, f1-score, etc. A maximum classification accuracy of 99.65% using the optimized model proposed by FCBF, PSO and ACO. The results show that the performance of the proposed system is superior to that of the classification technique presented above.

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Section: Related Workmentioning

confidence: 99%

Heart Disease Prediction and Classification Using Machine Learning Algorithms Optimized by Particle Swarm Optimization and Ant Colony Optimization

Khourdifi¹,

Bahaj²

2019

IJIES

211

View full text Add to dashboard Cite

show abstract

“…In addition, they did not use any textual data, but structured data were directly collected from patients. An interesting study on classification of heart disease diagnosis also used a hybrid classification consisting of an ensemble of a decision tree as a base classifier combined with naïve Bayes and Bayesian Neural Network methods (Liu et al, ). The nature of the structured clinical data in the study of Liu et al () demands such collection of classifiers.…”

Section: Background and Related Workmentioning

confidence: 99%

“…An interesting study on classification of heart disease diagnosis also used a hybrid classification consisting of an ensemble of a decision tree as a base classifier combined with naïve Bayes and Bayesian Neural Network methods (Liu et al, ). The nature of the structured clinical data in the study of Liu et al () demands such collection of classifiers. In the study of Tsai (), they developed four different types of ensemble classifiers including two clustering techniques, self‐organising maps and k ‐means, and three classification techniques, logistic regression, multilayer‐perceptron neural network, and decision trees, to predict bankruptcy.…”

Section: Background and Related Workmentioning

confidence: 99%

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A hybrid knowledge and ensemble classification approach for prediction of venous thromboembolism

et al. 2019

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Clinical narratives such as progress summaries, lab reports, surgical reports, and other narrative texts contain key biomarkers about a patient's health. Evidence‐based preventive medicine needs accurate semantic and sentiment analysis to extract and classify medical features as the input to appropriate machine learning classifiers. However, the traditional approach of using single classifiers is limited by the need for dimensionality reduction techniques, statistical feature correlation, a faster learning rate, and the lack of consideration of the semantic relations among features. Hence, extracting semantic and sentiment‐based features from clinical text and combining multiple classifiers to create an ensemble intelligent system overcomes many limitations and provides a more robust prediction outcome. The selection of an appropriate approach and its interparameter dependency becomes key for the success of the ensemble method. This paper proposes a hybrid knowledge and ensemble learning framework for prediction of venous thromboembolism (VTE) diagnosis consisting of the following components: a VTE ontology, semantic extraction and sentiment assessment of risk factor framework, and an ensemble classifier. Therefore, a component‐based analysis approach was adopted for evaluation using a data set of 250 clinical narratives where knowledge and ensemble achieved the following results with and without semantic extraction and sentiment assessment of risk factor, respectively: a precision of 81.8% and 62.9%, a recall of 81.8% and 57.6%, an F measure of 81.8% and 53.8%, and a receiving operating characteristic of 80.1% and 58.5% in identifying cases of VTE.

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“…9. Accuracy of stat log dataset using different classifiers Moreover, the results obtained are also compared to Liu et al [16], where relief and rough set (RFRS) are used for . Similarly, a Bounded Sum of Weighted Fuzzy Membership functions (BSWFM) together with Euclidean distance (ED) was used as FS on the Stat log dataset in the work of Lee [17].…”

Section: Fig 7 Accuracy Of Echocardiogram Dataset Using Different Cmentioning

confidence: 99%

Cuckoo inspired algorithms for feature selection in heart disease prediction

Usman

Yusof

Naim

2018

Int. J. Adv. Intell. Informatics

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Heart disease is a predominant killer disease in various nations around the globe. However, this is because the default medical diagnostic techniques are not affordable by common people. This inspires many researchers to rescue the situation by using soft computing and machine learning approaches to bring a halt to the situation. These approaches use the medical data of the patients to predict the presence of the disease or not. Although, most of these data contains some redundant and irrelevant features that need to be discarded to enhance the prediction accuracy. As such, feature selection has become necessary to enhance prediction accuracy and reduce the number of features. In this study, two different but related cuckoo inspired algorithms, cuckoo search algorithm (CSA) and cuckoo optimization algorithm (COA), are proposed for feature selection on some heart disease datasets. Both the algorithms used the general filter method during subset generation. The obtained results showed that CSA performed better than COA both concerning fewer number of features as well as prediction accuracy on all the datasets. Finally, comparison with the state of the art approaches revealed that CSA also performed better on all the datasets.

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A Hybrid Classification System for Heart Disease Diagnosis Based on the RFRS Method

Cited by 130 publications

References 48 publications

Heart Disease Prediction and Classification Using Machine Learning Algorithms Optimized by Particle Swarm Optimization and Ant Colony Optimization

Heart Disease Prediction and Classification Using Machine Learning Algorithms Optimized by Particle Swarm Optimization and Ant Colony Optimization

A hybrid knowledge and ensemble classification approach for prediction of venous thromboembolism

Cuckoo inspired algorithms for feature selection in heart disease prediction

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