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Title |
A Experimental Investigation of A Generic Method for Early Detection of Heart Disease |
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Author |
Venkateswara Rao Cheekati, Dr. D. Natarajasivan and Dr. S. Indraneel |
| Citation |
Vol. 22 No. 9 pp. 666-672 |
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Abstract |
The heart is the second most important organ in the body after the brain. Any trouble in the heart will eventually cause trouble in the rest of the body. As people who live in the modern world, we experience huge changes every day that affect us in some way or another. Heart disease, which kills people all over the world, is one of the top five diseases that kill the most people. So, being able to predict this disease is very important, as it will allow people to take the right steps at the right time. Data mining and machine learning are ways to find useful information in a huge amount of data and make it better. It is the first and most important step in figuring out how to define and find useful information and hidden patterns in databases. Optimization algorithms can be used to solve complex, non-linear problems because they are flexible and can be changed. Machine learning techniques are used in the medical sciences to help solve real health problems by predicting and treating diseases early on. In this paper, we use six different machine learning algorithms and then compare them based on how well they work. With a testing accuracy of 97.29%, decision tree is the best classifier out of all the others. |
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Keywords |
Heart Disease, Machine Learning Models, Python, Spyder |
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