DOI: https://doi.org/10.14710/medstat.16.2.124-135

PERFORMANCE OF NEURAL NETWORK IN PREDICTING MENTAL HEALTH STATUS OF PATIENTS WITH PULMONARY TUBERCULOSIS: A LONGITUDINAL STUDY

*Lalu Ramzy Rahmanda  -  Department of Statistics, Faculty of Mathematics and Natural Sciences, Brawijaya University, Indonesia
Adji Achmad Rinaldo Fernandes  -  Department of Statistics, Faculty of Mathematics and Natural Sciences, Brawijaya University, Indonesia
Solimun Solimun  -  Department of Statistics, Faculty of Mathematics and Natural Sciences, Brawijaya University, Indonesia
Lucius Ramifidiosa  -  Department of Mathematical Informatics, University of Antsiranana, Madagascar, Madagascar
Armando Jacquis Federal Zamelina  -  Department of Statistics, Brawijaya University, Indonesia
Open Access Copyright (c) 2023 MEDIA STATISTIKA under http://creativecommons.org/licenses/by-nc-sa/4.0.

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Abstract
Comorbidity between pulmonary tuberculosis and mental health status requires effective psychiatric treatment. This study aims to predict anxiety and depression levels in patients with pulmonary tuberculosis and consider future mental health treatment for patients. A sample of 60 pulmonary tuberculosis patients in Malang were involved and evaluated longitudinally every two weeks over 13 periods. In this study, we use the Generalized Neural Network Mixed Model (GNMM) to obtain better results in predicting anxiety and depression levels in patients with pulmonary tuberculosis and compare the results with the Generalized Linear Mixed Model (GLMM). The flexibility of GLMM in modeling longitudinal data, and the power of neural network in performing a prediction makes GNMM a powerful tool for predicting longitudinal data. The result shows that neural network's prediction performance is better than the classical GLMM with a smaller MSPE and fairly accurate prediction. The MSPEs of the three compared models: 1-Layer GNMM, 2-Layer, and GLMM, respectively are 0.0067, 0.0075, 0.0321 for the anxiety levels, and 0.0071, 0.0002, and 0.0775 for the depression levels. Furthermore, future research needs to investigate the data with a larger sample size or high dimensional data with large network architectures to prove the robustness of GNMM.
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Keywords: Artificial Neural Network; GLMM; Mental Health; Longitudinal Data Analysis; Pulmonary Tuberculosis

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