Effective heart disease prediction system using data mining techniques
Poornima Singh,1 Sanjay Singh,2 Gayatri S Pandi-Jain1 1L. J. Institute of Engineering and Technology, Gujarat Technological University, 2Institute of Life Sciences, School of Science and Technology, Ahmedabad University, Ahmedabad, Gujarat, India Abstract: The health care industries collect huge a...
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| Autores principales: | , , |
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| Formato: | article |
| Lenguaje: | EN |
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Dove Medical Press
2018
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| Acceso en línea: | https://doaj.org/article/ed1be6469c994aa8b5fc27ddfea10487 |
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| Sumario: | Poornima Singh,1 Sanjay Singh,2 Gayatri S Pandi-Jain1 1L. J. Institute of Engineering and Technology, Gujarat Technological University, 2Institute of Life Sciences, School of Science and Technology, Ahmedabad University, Ahmedabad, Gujarat, India Abstract: The health care industries collect huge amounts of data that contain some hidden information, which is useful for making effective decisions. For providing appropriate results and making effective decisions on data, some advanced data mining techniques are used. In this study, an effective heart disease prediction system (EHDPS) is developed using neural network for predicting the risk level of heart disease. The system uses 15 medical parameters such as age, sex, blood pressure, cholesterol, and obesity for prediction. The EHDPS predicts the likelihood of patients getting heart disease. It enables significant knowledge, eg, relationships between medical factors related to heart disease and patterns, to be established. We have employed the multilayer perceptron neural network with backpropagation as the training algorithm. The obtained results have illustrated that the designed diagnostic system can effectively predict the risk level of heart diseases. Keywords: data mining, neural network, multilayer perceptron neural network, backpropagation, disease diagnosis |
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