Active Prediction of Heart Disease using Techniques of Hybrid Machine Learning 
Jayantkumar A. Rathod1, Apoorva R2, M. Ramakrishna3, Gowthami H R4, Rachana T5
1Jayantkumar A*, Department of Information Science and Engineering, Alva’s Institute of Engineering and Technology, Moodbidri , India.
2Apoorva R, Department of Information Science and Engineering, Alva’s Institute of Engineering and Technology, Moodbidri, India.
3Gowthami H R, Department of Information Science and Engineering, Alva’s Institute of Engineering and Technology, Moodbidri, India.
4Ramakrishna M, Department of Information Science and Engineering, Alva’s Institute of Engineering and Technology, Moodbidri, India.
5Rachana T, Department of Information Science and Engineering, Alva’s Institute of Engineering and Technology, Moodbidri, India.
Manuscript received on June 01, 2020. | Revised Manuscript received on June 15, 2020. | Manuscript published on June 30, 2020. | PP: 836-840 | Volume-9 Issue-5, June 2020. | Retrieval Number: E9660069520/2020©BEIESP | DOI: 10.35940/ijeat.E9660.069520
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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: In this world one of the main sources of death is dependent on coronary illness happens in both men and women. It might cause because of the absence of data or inadequate data gave by the doctor in light of some innovation issue or because the prediction level is low. We have additionally observed the utilization of ML methods in ongoing advancements in different Internet of Things (IoT) fields. Different examinations just give a brief look at anticipating coronary illness utilizing ML methods. In this paper, we are looking at how this hybrid method is better than utilizing a single calculation which gives higher exactness up to 88.7% than contrast with different procedures
Keywords: Algorithms of classification, cardiovascular disease (CVD), Machine learning, Model of prediction, Prediction of heart disease, Selection of functions