Effective Cardiovascular Disease Prediction using Hybrid Machine Learning Techniques
Madhavi Veeranki1, Jayanag Bayana2
1Madhavi Veeranki*, Studying Master of Technology, Department of computer science and engineering ,velagapudi Ramakrishna Siddhartha engineering college , kanuru, Vijayawada.
2Jayanag Bayana, Sr. Asst. Professor in Velagapudi Ramakrishna Siddhartha Engineering college, dept of computer science and engineering, kanuru, Vijayawada.
Manuscript received on March 30, 2020. | Revised Manuscript received on April 05, 2020. | Manuscript published on April 30, 2020. | PP: 1555-1558 | Volume-9 Issue-4, April 2020. | Retrieval Number: D8777049420/2020©BEIESP | DOI: 10.35940/ijeat.D8777.049420
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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 today’s era deaths due to heart disease has become a major issue approximately one person dies per minute due to heart disease. This is considering both male and female category and this ratio may vary according to the region also this ratio is considered for the people of different age groups.Heart disease is one of the most fatal problems in the whole world, which cannot be seen with a naked eye and comes instantly when its limitations are reached.There are various data mining and machine learning techniques and tools available to extract effective knowledge from databases and to use this knowledge for more accurate diagnosis and decision making.In this paper, we propose a novel method that aims at finding significant features by applying machine learning techniques resulting in improving the accuracy in the prediction of cardiovascular disease. We produce an enhanced performance level with an accuracy through the prediction model for heart disease with the proposed method hybrid random forest with a linear model.In this paper commonly used machine learning techniques and their complexities are summarized.
Keywords: Machine Learning, Heart Disease Prediction, Classification Algorithms.