Early detection of coronary heart disease using ensemble techniques

Shorewala, Vardhan

doi:10.1016/j.imu.2021.100655

Cited by 122 publications

(50 citation statements)

References 13 publications

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“…The number of publications on this phenomenon decreased from 12 in 2019 to 7 in 2020. CAD presence prediction 23 [25] Heart disease prediction 5 [26] Coronary heart disease prediction 24 [27] Heart disease prediction 6 [28] CHD detection 25 [29] CHD prediction 7 [30] CAD prediction 26 [31] CHD prediction 8 [32] predict coronary heart disease 27 [33] prediction of CHD 9 [16] CHD Prediction based on risk factors 28 [34] classification of coronary artery disease medical data sets [1] Accuracy of ML algorithms for predicting clinical events 29 [35] Prediction of CHD [17] methodology of predicting CHD 30 [36] CAD detection [37] CAD detection 31 [2] CHD Prediction [38] prediction of heart diseases 32 [39] Heart Disease Diagnosis [40] prediction of heart diseases 33 [41] CHD prediction [42] CAD diagnosis 34 [43] CHD prediction [44] Prediction of CHD 35 [45] NN-based prediction of CHD [46] Diagnosing CHD 36 [47] Prediction of CHD [48] prediction of heart disease 37 [49] Prediction of CHD [50] CHD Diagnosis…”

Section: Resultsmentioning

confidence: 99%

Application of Machine Learning Algorithms in Coronary Heart Disease: A Systematic Literature Review and Meta-Analysis

Kutiame¹,

Millham²,

Adekoya³

et al. 2022

IJACSA

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Section: Resultsmentioning

confidence: 99%

Application of Machine Learning Algorithms in Coronary Heart Disease: A Systematic Literature Review and Meta-Analysis

Kutiame¹,

Millham²,

Adekoya³

et al. 2022

IJACSA

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“…The performance comparison of the proposed ensemble RSS‐KNN scheme over existing NB, 13 SVM‐XGD, 14 LR, 15 RF, 15 AGAFL, 16 HRLFS, 17 and DT 18 schemes are exposed in Table 3. The effectiveness of the proposed scheme is compared with three standard benchmark datasets like Cleveland, Statlog, and UCI ML repository.…”

Section: Resultsmentioning

confidence: 99%

“…In Reference 13, a Naive Bayes (NB) classifier has been proposed to predict heart disease. It employed the cardiovascular disease repository dataset for the evaluation phase that contains 70,000 records of patient data.…”

Section: Related Workmentioning

confidence: 99%

An automated clinical decision support system for predicting cardiovascular disease using ensemble learning approach

Kannan¹

2022

Concurrency and Computation

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With the vast advancements in the medical domain, earlier prediction of disease plays a substantial role in enhancing the healthcare quality and assists in taking better decisions making during emergency times. Most of the existing research concentrates on modeling an automated prediction model for heart disease and the risk factors. Nevertheless, accurate classification is a vital challenge in heart disease diagnosis where the managing of high‐dimensional data increases the execution time of existing classifiers. In this paper, a new ensemble model has been proposed with the aid of random subspace and K‐nearest neighbor (RSS‐KNN) scheme for earlier prediction of heart disease. Primarily, the proposed scheme implements an isolation‐based outlier removal mechanism to eradicate the noises and outliers in the distributed data. Subsequently, the essential features are identified using RSS by varying the testing and training errors in the evaluation phase. The extracted features are then fed into KNN for the accurate classification of heart disease. Finally, an enhanced squirrel optimizer has been employed in the proposed scheme to obtain the global results which balance the exploration as well as exploitation issues and eliminate the over‐fitting problems. The simulation results manifest that the accuracy (without features) of the proposed ensemble RSS‐KNN scheme in the UCI ML dataset is 97.65%, accuracy (with features) is 98.56%, and specificity is 98.10% when compared with existing state‐of‐the‐art classifiers.

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“…TPR is shown against FPR on the Receiver Operator Characteristic (ROC) curve, which is used to evaluate binary classification problems [13]. The harmonic mean of precision and sensitivity is the F-score [8].…”

Section: Rocmentioning

confidence: 99%

Predicting Heart Diseases through Feature Selection and Ensemble Classifiers

Diwan

Thakur²,

Sahu³

et al. 2022

J. Phys.: Conf. Ser.

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Heart diseases or Cardiovascular Diseases are the leading cause of death globally. Amid the Covid-19 pandemic, the toll has further increased and is prevalent among all age groups. The reasons are associated with various side effects of lockdown or socio-economic affairs. It becomes extremely important to strengthen our research on diagnosis systems to timely and accurately identify the disease. This paper is an attempt to predict a healthy or heart patient using ensemble machine learning methods depending on selected features. The proposed model shows that after performing feature selection the ensemble models give optimum accuracy with significantly lesser features.

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Early detection of coronary heart disease using ensemble techniques

Cited by 122 publications

References 13 publications

Application of Machine Learning Algorithms in Coronary Heart Disease: A Systematic Literature Review and Meta-Analysis

Application of Machine Learning Algorithms in Coronary Heart Disease: A Systematic Literature Review and Meta-Analysis

An automated clinical decision support system for predicting cardiovascular disease using ensemble learning approach

Predicting Heart Diseases through Feature Selection and Ensemble Classifiers

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