Different Machine Learning Models Based Heart Disease Prediction
Arunpradeep N.1, G. Niranjana2

1Arunpradeep.N, Master Of Technology, Department of Internet of Things, SRM Institute of Science and Technology, KTR Campus, Chennai, Tamilnadu, India.
2Dr. G. Niranjana, Associate Professor, Department of Computer Science and Engineering, SRM Institute of Science and Technology, KTR Campus, Chennai, Tamilnadu, India
Manuscript received on February 02, 2020. | Revised Manuscript received on February 10, 2020. | Manuscript published on March 30, 2020. | PP: 544-548 | Volume-8 Issue-6, March 2020. | Retrieval Number: F7310038620/2020©BEIESP | DOI: 10.35940/ijrte.F7310.038620

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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 related disease is one of the crucial reasons for high amount of people’s death in the whole countries and it’s considered as life forbidding disorder, in addition to that this effect takes place in whole earth. Heart disease will affect the early stage of age peoples also. Thus, heart related disease creates the more challenges to people living and identify the causes and detection step is more important in nowadays. So, we need to develop of automatic system with more accurate and reliable for early detection of heart disease. For this reason, various machine learning models are developed to predict heart related disease; different medical data package is processed to automatic analysis with get more accuracy. In this paper, we discuss the available machine learning models such as KNN, SVM, DT and RF algorithms for prognosis of heart disease with high certitude, precision and recall.
Keywords: Heart Disease prediction, KNN, SVM, Decision Tree, Random Forest, machine learning.
Scope of the Article: Machine Learning.