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Efficient Diagnostic Cardiac System using Machine Learning Approach |
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| รหัสดีโอไอ | |
| Creator | Mujtaba Ashraf Qureshi, Azad Kumar Shrivastava |
| Title | Efficient Diagnostic Cardiac System using Machine Learning Approach |
| Contributor | - |
| Publisher | TuEngr Group |
| Publication Year | 2563 |
| Journal Title | International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies |
| Journal Vol. | 11 |
| Journal No. | 15 |
| Page no. | 11A15F: 1-8 |
| Keyword | Data mining, neural networks, feature selection, WEKA tool, Cardiovascular disease prediction, Cardiac disease prediction. |
| URL Website | http://TuEngr.com/Vol11_15.html |
| Website title | ITJEMAST V11A(15) 2020 @ TuEngr.com |
| ISSN | 2228-9860 |
| Abstract | Heart disease is considered one of the ultimate threats to human life. To predict cardiac diseases in the early stages has become a challenge to medical science. Machine learning has acted as a rescuer to assist and develop various cardiac diagnostic systems. Data mining techniques mostly used as a synonym to machine learning plays an important role to mine useful knowledge. However, machine learning (ML) emphasis more on the prediction of diverse diseases. In this research work, three models are devised to predict cardiovascular diseases using artificial neural networks. Models are devised based on the application of a different number of hidden layers. The backpropagation algorithm is used to calculate the desired value by the adjustment of weights of the neurons in the network. In the very last stage of the experimental work performance measures of the three devised models are compared to reach the most efficient model. |
