Early Depression Prediction among Nigerian University Students Using Adaptive Neuro-Fuzzy Inference System (ANFIS)

Samuel A. Robinson *

Department of Computer Science, University of Uyo, Nigeria.

Akanimoh E. Udoh

Department of Computer Engineering, Heritage Polytechnic, Eket, Nigeria.

Emmanuel A. Dan

Department of Computer Science, University of Uyo, Nigeria.

Pius U. Ejodamen

Department of Computer Science, University of Uyo, Nigeria.

Kingsley U. Joseph

Department of Computer Science, University of Uyo, Nigeria.

Doris G. Asuquo

Department of Computer Science, College of Education, Afaha Nsit, Nigeria.

*Author to whom correspondence should be addressed.


Abstract

Depression is a mental disorder characterized by a sad mood, irritability, anger, agitations, loss of interest or pleasure, reduced energy, feelings of guilt, low self-esteem, troubled sleep, appetite loss, and poor attentiveness. The effects of late diagnosis of depression in Nigerian students have posed threats to the academic performance of the students, economic growth, and security threats. To address this challenge, an ANFIS model for early detection of depression among Nigerian Students is proposed. This aids in the reduction and possible elimination of prevalent cases of depression-related dangers among students in tertiary institutions. ANFIS is utilized because of its transparency and ability to classify and identify hidden symptoms of depression, and its tendency for reduced memorization errors for users. The database was developed to hold user data, symptoms, and prescriptions and linked to the ANFIS framework to enable the diagnosis of early-phase depression. Data was collected from the University of Uyo primary health care center, and the University of Uyo Teaching Hospital (UUTH). The ANFIS model implementation was implemented in MATLAB while the application forming the input interface was implemented with JAVA. The dataset for training was passed through ANFIS for 10 epochs and upon completion the system had a training error of 6.0138e-0.5 and an average testing error of 4.6648 on the test data, these results indicate that the system possessed 95% classification accuracy in the detection of early depression in Nigerian students.

Keywords: Depression, adaptive neuro-fuzzy inference system, diagnosis


How to Cite

Robinson , S. A., Udoh , A. E., Dan , E. A., Ejodamen , P. U., Joseph , K. U., & Asuquo , D. G. (2024). Early Depression Prediction among Nigerian University Students Using Adaptive Neuro-Fuzzy Inference System (ANFIS). Journal of Advances in Mathematics and Computer Science, 39(2), 1–10. https://doi.org/10.9734/jamcs/2024/v39i21864

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References

Marcus M, Yasamy MT, Van Ommeren MV, Chisholm D, Saxena S. Depression: A global public health concern; 2012.

Lotfi MH, Aminian AH, Ghomizadea A, Zarea S. Prevalence of depression amongst students of Shaheed Sadoughi University of Medical Sciences, Yazd, Iran. Iranian Journal of Psychiatry and Behavioral Sciences. 2010;4(2):51-5.

Gashaw Y. Depression among addis ababa university students sidist kilo campus: Prevalence, gender difference and other associated factors (Doctoral dissertation, Addis Ababa University); 2015.

Ibrahim AK, Kelly SJ, Adams CE, Glazebrook C. A systematic review of studies of depression prevalence in university students. Journal of Psychiatric Research. 2013;47(3):391-400.

Kwiatkowska M, Kielan K, Michalik K. A fuzzy-semiotic framework for modeling imprecision in the assessment of depression. In IFSA/EUSFLAT Conf. 2009;1717-1722.

Şahin M, Erol R. A comparative study of neural networks and ANFIS for forecasting attendance rate of soccer games. Mathematical and Computational Applications. 2017;22(4):43.

WHO. Depression, mental health: Depression; 2012. Available:http://www.who.int/mediacentre/factsheets/fs369/en/

Andrews G, Cuijpers P, Craske MG, McEvoy P, Titov N. Computer therapy for the anxiety and depressive disorders is effective, acceptable and practical health care: a meta-analysis. PloS one. 2010;5(10):e13196. Available:http://doi.org/10.1371/journal.pone.0013196

Bilsen J. Suicide and youth: risk factors. Frontiers in psychiatry. 2018;9:540. DOI: 10.3389/fpsyt.2018.00540

Gashaw Y. Depression among addis ababa university students sidist kilo campus: Prevalence, gender difference and other associated factors (Doctoral dissertation, Addis Ababa University); 2015.

Sarokhani D, Delpisheh A, Veisani Y, Sarokhani MT, Manesh RE, Sayehmiri K. Prevalence of depression among university students: a systematic review and meta-analysis study. Depression research and Treatment. 2013;2013. DOI: org/10.1155

Chattopadhyay S. A neuro-fuzzy approach for the diagnosis of depression. Applied Computing and Informatics. 2017;13(1):10-18.

Ashish K, Dasari A, Chattopadhyay S, Hui NB. Genetic-neuro-fuzzy system for grading depression. Applied Computing and Informatics. 2018;14(1):98-105.

Ekong VE, Ekong UO, Uwadiae EE, Abasiubong F, Onibere EA. A fuzzy inference system for predicting depression risk levels. African Journal of Mathematics and Computer Science Research. 2013;6(10):197-204.

Ekong Victor E, Udoinyang G Inyang, Emmanuel A Onibere. Intelligent decision support system for depression diagnosis based on neuro-fuzzy-CBR hybrid. Modern Applied Science. 2012;6(7):79.

Şahin M, Erol R. A comparative study of neural networks and ANFIS for forecasting attendance rate of soccer games. Mathematical and Computational Applications. 2017;22(4):43.

Osubor VI, Egwali AO. A neuro fuzzy approach for the diagnosis of postpartum depression disorder. Iran Journal of Computer Science. 2018;1:217-225.

Frey N, Fisher D, Smith D. All learning is social and emotional: Helping students develop essential skills for the classroom and beyond. Ascd; 2019.