Abstract
The World Health organization estimates that heart disease is liable for the death of 12 million people per year throughout the world. Nearly half of all deaths are caused by cardiovascular disease each year. The earlier cardiovascular disease may be detected and treated, the lower the risk of significant outcomes for individuals who are already at risk. Developing a better heart disease prediction model can be extremely beneficial in predicting cardiovascular disease risk more accurately. Authors developed Gated Recurrent Unit (GRU) and a random forest (RF) based heart disease prediction model in this study to weed out risk factors for the heart disease. After screening out the main attributes, the proposed approach GRU-RF is tested against traditional Deep Neural Network, and K-nearest neighbour algorithms. The prediction accuracy of proposed approach is 87%, and then, finally, authors selected the best accurate algorithm to develop a model for the prediction of heart disease in the future.
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Acknowledgment
This research was supported by the Universiti Tun Hussein Onn Malaysia (UTHM) through the Multidisciplinary Research Grant (MDR) (Vote H494).
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Javid, I., Ghazali, R., Zulqarnain, M., Husaini, N.A. (2022). Deep Learning GRU Model and Random Forest for Screening Out Key Attributes of Cardiovascular Disease. In: Ghazali, R., Mohd Nawi, N., Deris, M.M., Abawajy, J.H., Arbaiy, N. (eds) Recent Advances in Soft Computing and Data Mining. SCDM 2022. Lecture Notes in Networks and Systems, vol 457. Springer, Cham. https://doi.org/10.1007/978-3-031-00828-3_16
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DOI: https://doi.org/10.1007/978-3-031-00828-3_16
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