An Optimized DBN-based Coronary Heart Disease Risk Prediction

Kahyun Lim, Byung Mun Lee, Ungu Kang, Youngho Lee

Abstract


Coronary Heart Disease (CHD) is the world’s leading cause of death according to a World Health Organization (WHO) report. Despite the evolution of modern medical technology, the mortality rate of CHD has increased. Nevertheless, patients often do not realize they have CHD until their condition is serious due to the complexity, high cost, and the side effects of the diagnosis process. Thus, research on predicting CHD risk has been conducted. The Framingham study is a widely-accepted study in this field. However, one of its limitations is its overestimation of risk, which threatens its accuracy. Therefore, this study suggests a more advanced CHD risk prediction algorithm based on Optimized-DBN (Deep Belief Network). Optimized- DBN is an algorithm to improve performance by overcoming the limitations of the existing DBN. DBN does not have the global optimum values for number of layers and nodes, which affects research results. We overcame this limitation by combining with a genetic algorithm. The result of genetic algorithm for deriving the number of layers and nodes of Optimized-DBN for CHD prediction was 2 layers, 5 and 7 nodes to each layers. The accuracy of the CHD prediction algorithm based on Optimized- DBN which is developed by applying results of genetic algorithm was 0.8924, which is better than Framingham’s 0.5015 and DBN’s 0.7507. In the case of specificity, Optimized-DBN based CHD prediction was 0.7440, which was slightly lower than 0.8208 of existing DBN, but better than Framingham’s 0.65. In the case of sensitivity, Optimized-DBN is 0.8549, which is better than Framingham 0.4429 and DBN 0.7468. AUC of suggesting algorithm was 0.762, which was much better than Framingham 0.547 and DBN 0.570.

Keywords


Artificial Neural Networks (ANN), Deep Belief Network (DBN), Coronary Heart Disease (CHD), computational intelligence, genetic algorithm, CHD prediction

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References


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DOI: https://doi.org/10.15837/ijccc.2018.4.3269



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