Cardiovascular Disease Recognition through Machine Learning Algorithms
Rajatdeep Kaur1, Kamaljit Kaur2
1Rajatdeep Kaur*, Department of Computer Engineering and Technology, Guru Nanak Dev University, Amritsar, India.
2Kamaljit Kaur, Department of Computer Engineering and Technology, Guru Nanak Dev University, Amritsar, India.
Manuscript received on March 28, 2020. | Revised Manuscript received on April 25, 2020. | Manuscript published on April 30, 2020. | PP: 2109-2115 | Volume-9 Issue-4, April 2020. | Retrieval Number: D9149049420/2020©BEIESP | DOI: 10.35940/ijeat.D9149.049420
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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: he heart is more important to the human body than any other circulatory organs. Its function is to provide and pump blood to other organs and brain. So it is very important to have a healthy heart but researches revealed the risk of heart failure increases every day starting from age 30. Many heart specialist can diagnose heart disease with their experience and skills. But some experts lacking the talent or knowledge to predict cardiovascular disease in the early stages, a small mistake can cost a patient’s life. Therefore, it is necessary to use specific methods and algorithmic tools to estimate the occurrence of cardiac disorders in the early stages. Different Algorithms for machine learning and data analysis are beneficial in predicting various diseases from patient’s data, managed by the Medical Center or hospitals. The data obtained may also help to assess the presence of the disease in the future. Heart Disease or Cardiac related issues can be analyzed by variety of machine learning techniques, Instance Artificial Neural Network, Decision Tree, Random forest, K-nearest neighbor, Naïve Bayes and Support Vector Machine. This study establishes a theoretical understanding of existing algorithms and provides a general understanding of existing work.
Keywords: Cardiovascular Disease, Data Mining, Machine Learning.