An Efficient Ensemble Classifier for Heart Disease Diagnosis and early Prediction
S. Brindha1, T. Ajisha2
1S.Brindha*, Computer Science and Applications, St.Peter’s Institute of Higher Education and Research, Avadi, Chennai, Tamil Nadu, India.
2T.Ajisha, Computer Science and Applications, St.Peter’s Institute of Higher Education and Research, Avadi, Chennai, Tamil Nadu, India.
Manuscript received on October 06, 2020. | Revised Manuscript received on October 25, 2020. | Manuscript published on November 30, 2020. | PP: 109-114 | Volume-9 Issue-4, November 2020. | Retrieval Number: 100.1/ijrte.D4824119420 | DOI: 10.35940/ijrte.D4824.119420
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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
Keywords: Support Vector Classification, Weighted Random Forest, Machine Learning, Artificial Intelligence, and Heart Disease Pattern Prediction.