Heart Disease Prediction Method using Hybrid Classifier
Saloni Kapoor1, Ashwinder Tanwar2
1Saloni Kapoor, University of Engineering, Chandigarh University, Mohali, India.
2Ashwinder Tanwar, University of Engineering, Chandigarh University, Mohali, India.
Manuscript received on 21 September 2019 | Revised Manuscript received on 30 September 2019 | Manuscript Published on 01 October 2019 | PP: 57-61 | Volume-8 Issue-9S4 July 2019 | Retrieval Number: I11090789S419/19©BEIESP | DOI: 10.35940/ijitee.I1109.0789S419
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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: The data mining is the approach which can extract useful information from the data. The following research work that has been described is related to the heart disease prediction. The prediction analysis is the approach which can predict future possibilities based on the current information. For the heart disease prediction the classifier that is designed in this research work is hybrid classifier. The hybrid classifier is combination of random forest and decision tree classifier. Moreover, the heart disease prediction technique has three steps which are data pre-processing, feature extraction and classification. In this paper, random forest classifier is applied for the feature extraction and decision tree classifier is applied for the generation of prediction results. However, random forest classifier will extract the information and decision tree will generate final classifier result. We have proposed a hybrid model that has been implemented in python. Moreover, the results are compared with Support Vector Machine (SVM) and K-Nearest Neighbor classifier (KNN).
Keywords: Hybrid classifier, Random Forest classifier, Decision Tree Classifier, Heart Disease Prediction.
Scope of the Article: Classification