Empirical Analysis of Cardiovascular Diseases using Machine Learning and Soft Computing Techniques
Raghavendra Kumar1, Ashish Mishra2, Himanshu Rathore3
1Raghavendra Kumar*, Department of Information Technology, KIET Group of Institutions, Ghaziabad, India.
2Ashish Mishra, Department of Information Technology, KIET Group of Institutions, Ghaziabad, India.
3Himanshu Rathore, Department of Information Technology, KIET Group of Institutions, Ghaziabad, India.
Manuscript received on September 25, 2019. | Revised Manuscript received on October 15, 2019. | Manuscript published on October 30, 2019. | PP: 3944-3948 | Volume-9 Issue-1, October 2019 | Retrieval Number: A1494109119/2019©BEIESP | DOI: 10.35940/ijeat.A1494.109119
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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: Cardiovascular diseases are a one of the most exigent issue in healthcare domain. There have been various multidisciplinary approaches proposed and applied to reduce the mortality rate. As per literature and current study machine learning and soft computing techniques are efficient and widely accepted approaches in research community. This paper identifies and compares the various techniques of machine learning using Random Forest (RF), Support Vector Machine (SVM), XG Boost and Artificial Neural Network (ANN) and uncovers the F1 score, recall, precision to predict efficient and more accurate result. The results are further compared with existing benchmark models and showed significant improvement in heart disease prediction of patient.
Keywords: Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Network (ANN).