Prediction of Elementary School Students' Mental Health using Decision Tree Algorithm with K-Fold Cross-Validation in Bone Bolango Regency, Gorontalo Province

Salahuddin Liputo; Franky  Tupamahu; Wahyudin  Hasyim; Sri Ariyanti  Sabiku; Rahmawaty  Parman; Aan  Hanapi

doi:10.37899/journallamultiapp.v4i6.1005

Salahuddin Liputo Psychology Department, Universitas Muhammadiyah Gorontalo
Franky Tupamahu Information Systems Department, Universitas Muhammadiyah Gorontalo
Wahyudin Hasyim Information Systems Department, Universitas Muhammadiyah Gorontalo
Sri Ariyanti Sabiku Tourism Department, Universitas Negeri Gorontalo
Rahmawaty Parman Psychology Department, Universitas Muhammadiyah Gorontalo
Aan Hanapi Psychology Department, Universitas Muhammadiyah Gorontalo

DOI: https://doi.org/10.37899/journallamultiapp.v4i6.1005

Keywords: Mental Health 2, SEHS-S, Machine Learning, Decision Trees

Abstract

Mental health is a fundamental component of the World Health Organization's definition of health, encompassing not only freedom from illness but also well-being in physical, mental, and social dimensions. In today's modern society, mental health has become a paramount issue, as its soundness enables individuals to realize their own potential, cope with normal life pressures, work productively, and contribute effectively to their communities. In Indonesia, mental health-related challenges are associated with the absence of a reliable mental health detection tool. Conversely, abroad, there has been a substantial amount of research focused on innovative technology-based mental health detection using Machine Learning. This study aims to predict mental health using the Social Emotional Health Survey-Secondary (SEHS-S) as the evaluation criterion for prediction through Machine Learning. The Decision Tree algorithm is employed, and the prediction model is tested using K-Fold Cross-Validation, resulting in 8 folds with an accuracy rate of 78.61%.

References

Ridlo, I. A., & Zein, R. A. (2018). Arah Kebijakan Kesehatan Mental: Tren Global dan Nasional Serta Tantangan Aktual. Buletin Penelitian Kesehatan, 46(1), 45-52.

World Health Organization (2013). Mental Health Action Plan/MHAP.

Riset Kesehatan Dasar (Riskesdas) (2018). Badan Penelitian dan Pengembangan Kesehatan Kementerian RI tahun 2018.

Mastoras, R. E., Iakovakis, D., Hadjidimitriou, S., Charisis, V., Kassie, S., Alsaadi, T., ... & Hadjileontiadis, L. J. (2019). Touchscreen typing pattern analysis for remote detection of the depressive tendency. Scientific reports, 9(1), 1-12.

Zhou S, Zhao J and Zhang L (2022) Application of Artificial Intelligence on Psychological Interventions and Diagnosis: An Overview. Front. Psychiatry 13:811665. doi: 10.3389/fpsyt.2022.811665

Vaishnavi, K., Kamath, U. N., Rao, B. A., & Reddy, N. S. (2022). Predicting mental health illness using machine learning algorithms. In Journal of Physics: Conference Series. 161 (10), 012021. IOP Publishing.

Bedi, G., Carrillo, F., Cecchi, G. A., Slezak, D. F., Sigman, M., Mota, N. B., & Corcoran, C. M. (2015). Automated analysis of free speech predicts psychosis onset in high-risk youths. npj Schizophrenia, 1(1), 1-7.

Corcoran CM, Carrillo F, Slezak DF, Bedi G, Klim C, Javitt DC, Bearden CE, Cecchi GA. (2018). Prediction of psychosis across protocols and risk cohorts using automated language analysis. World Psychiatry, 17:67-75

Laijawala, V., Aachaliya, A., Jatta, H., & Pinjarkar, V. (2020). Classification algorithms based mental health prediction using data mining. In 2020 5th international conference on communication and electronics systems (ICCES) (pp. 1174-1178). IEEE.

Furlong, M. J., Nylund-Gibson, K., Dowdy, E., Wagle, R., Hinton, T., & Carter, D. (2020). Modification and standardization of Social Emotional Health Survey Secondary— 2020 edition. Santa Barbara, CA, University of California Santa Barbara, International Center for School Based Youth Development.