5835262
doi
10.35940/ijrte.D4824.119420
oai:zenodo.org:5835262
Blue Eyes Intelligence Engineering and Sciences Publication(BEIESP)
Publisher
T. Ajisha
Computer Science and Applications, St.Peter's Institute of Higher Education and Research, Avadi, Chennai, Tamil Nadu, India.
An Efficient Ensemble Classifier for Heart Disease Diagnosis and early Prediction
S. Brindha
Computer Science and Applications, St.Peter's Institute of Higher Education and Research, Avadi, Chennai, Tamil Nadu, India.
issn:2277-3878
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Support Vector Classification, Weighted Random Forest, Machine Learning, Artificial Intelligence, and Heart Disease Pattern Prediction.
<p>Heart Disease is one of the most significant causes of mortality in the world today. Prediction and Diagnosis of Cardiovascular disease is considered as one of the major challenges in the Medical Field especially for Cardiologists. Artificial Intelligence and Machine learning (ML) was popularly employed for pattern prediction and it was noticed that these Intelligent Mechanisms were used in Medical Feld for better Heart Disease Pattern Prediction. Thus more researchers were focusing Machine Learning based Data Mining Classifiers for Heart Disease Pattern Prediction and Diagnosis in the healthcare Industry especially for Cardiologists. This research work identified the recently proposed Hybrid Random Forest with a Linear Model (HRFLM) Classifier for improving the classification accuracy for the cardiovascular disease patterns prediction well in advance and Diagnosis as well. However, it was noticed that for improving the performances better in terms of Accuracy, Sensitivity, Specificity, Precision, FScore and False Positive Rate FPR, needed an efficient classifier. Thus this work developed and implemented an efficient Classifier ensemble Nu-SVC Classifier and Weighted Random Forest Classifier. From the experimental results, it was noticed that the proposed Ensemble Classifier performs better as compared with that of existing Hybrid Classifier in terms of in terms of Accuracy, Sensitivity, Specificity, Precision, FScore and False Positive Rate FPR</p>
Zenodo
2020-11-30
info:eu-repo/semantics/article
5835261
1641908928.052632
829333
md5:e1a01ef68da78152cde4dbfa1466f38e
https://zenodo.org/records/5835262/files/D4824119420.pdf
public
2277-3878
Is cited by
issn
International Journal of Recent Technology and Engineering (IJRTE)
9
4
109-114
2020-11-30