Prediction of Serious Depressive Symptoms by Blood Test and Environmental Factor in Adult Men and Women 


Vol. 43,  No. 8, pp. 1368-1377, Aug.  2018
10.7840/kics.2018.43.8.1368


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  Abstract

Severe depression is one of the mental illnesses that attract attention as a serious social issue. These mental illness may be related to physical illness or environmental factors. In this paper, we perform a modeling to predict the severity of depression due to the individual physical factors and the environmental factors, analyze the accuracy, sensitivity, and specificity of the proposed models. In particular, we use the Artificial Neural Network (ANN) and Deep Neural Network (DNN) models of the neural networks for predicting the severity of depression. Comparing the accuracy results of ANN and DNN models using various optimizers, the predictive accuracy of the ANN model is 72.27% and the predictive accuracy of the DNN model is 76.08%, respectively. The accuracy of prediction of machine learning algorithm for predicting serve depression is presented by comparing the Area Under Curve (AUC) values for proposed models.

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  Cite this article

[IEEE Style]

M. Ji and H. Park, "Prediction of Serious Depressive Symptoms by Blood Test and Environmental Factor in Adult Men and Women," The Journal of Korean Institute of Communications and Information Sciences, vol. 43, no. 8, pp. 1368-1377, 2018. DOI: 10.7840/kics.2018.43.8.1368.

[ACM Style]

Minjun Ji and Hyunhee Park. 2018. Prediction of Serious Depressive Symptoms by Blood Test and Environmental Factor in Adult Men and Women. The Journal of Korean Institute of Communications and Information Sciences, 43, 8, (2018), 1368-1377. DOI: 10.7840/kics.2018.43.8.1368.

[KICS Style]

Minjun Ji and Hyunhee Park, "Prediction of Serious Depressive Symptoms by Blood Test and Environmental Factor in Adult Men and Women," The Journal of Korean Institute of Communications and Information Sciences, vol. 43, no. 8, pp. 1368-1377, 8. 2018. (https://doi.org/10.7840/kics.2018.43.8.1368)

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pISSN : 1226 - 4717
eISSN : 2287 - 3880