International Journal of Immunology
Volume 6, Issue 1, March 2018, Pages: 5-16
Received: Oct. 24, 2017;
Accepted: Nov. 9, 2017;
Published: Jan. 23, 2018
Views 2718 Downloads 266
Mhambe Priscilla Dooshima, Department of Computer Science and Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria
Egejuru Ngozi Chidozie, Department of Computer Science and Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria
Balogun Jeremiah Ademola, Department of Computer Science and Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria
Olusanya Olayinka Sekoni, Department of Computer Science, Tai Solarin University of Education, Ijebu Ode, Nigeria
Idowu Peter Adebayo, Department of Computer Science and Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria
This study identified the risk factors for mental illness and formulated a predictive model based on the identified variables. The study simulated the formulated model and validated the model with a view to developing a model for predicting the risk of mental illness. Following the review of literature in order to understand the body of knowledge surrounding mental illness and their corresponding risk factors, interview with mental experts was conducted in order to validate the identified variables. Naïve Bayes’ and the Decision Trees’ Classifiers were used to formulate the predictive model for the risk of mental illness based on the identified and validated variables using the WEKA software. Data was collected from 30 patients with an almost equal distribution of no, low, moderate and high risk of mental illness cases. The results showed that there were three classes of risk factors associated with mental illness, namely: biological factors, psychological factors and environmental factors. The results further showed that the formulation with Decision Trees Classifiers revealed the most relevant variables for the risks of mental illness such as losing anyone close. C4.5 decision trees algorithm with an accuracy of 83.3% outperformed the Naïve Bayes’ algorithm which had an accuracy of 76.7%. The study concluded that the variables identified by the C4.5 Decision Trees algorithm can assist mental health experts to apply the rules deduced by the algorithm for the early detection of mental illness.
Mhambe Priscilla Dooshima,
Egejuru Ngozi Chidozie,
Balogun Jeremiah Ademola,
Olusanya Olayinka Sekoni,
Idowu Peter Adebayo,
A Predictive Model for the Risk of Mental Illness in Nigeria Using Data Mining, International Journal of Immunology.
Vol. 6, No. 1,
2018, pp. 5-16.
World Health Organization (2011a). Political Declaration of the High-level Meeting of the General Assembly on the Prevention and Control of Non-Communicable Diseases. 66th Session of the Unites Nations General Assembly. New York: WHO.
Gordon, A. (2013). Mental Health Remains an Invisible Problem in Africa. Think Africa Press. Retrieved from http://thinkafricapress.com on May 12, 2017.
Arboleda-Florez, J. (2002). What Causes Stigma. World Psychiatry 1 (1): 25–26.
Corrigan, P. W., Druss, B. G. and Perlick, D. A. 2014. The Impact of Mental Illness Stigma on Seeking and Participating in Mental Health Care. Psychological Science in the Public Interest, 15 (2) 37–70: sagepub.com/journalsPermissions.nav.
Fournier, O. A. (2011). The Status of Mental Health Care in Ghana, West Africa and Signs and Progress in the Greater Accra Region. Berkeley Undergraduate Journal 24 (3): 1–6.
Naifeh, J. A., Colpe, C. L. J., Aliaga, P. A., Sampson, N. A., Heeringa, S. G., Stein, M. B., Ursano, R. J., Fullerton, C. S., Nock, M. K., Schoenbaum, M. and Zaslavsky, A. M., 2016. Barriers to initiating and continuing mental health treatment among soldiers in the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS). Military medicine, 181 (9), p.1021.
Hanlon, C., Wondimagegn, D. and Alem, A. (2010). Lessons Learned in Developing Community Mental Health Care in Africa. World Psychiatry 9 (3): 185–189.
World Health Organization (2011b). WHO African Regional Ministerial Consultation on Non-communicable Diseases. Brazzaville, Congo. Brazzaville: WHO Regional Office for Africa.
World Health Organization (2015). Mental health atlas 2014. World Health Organization, Reterived from http://apps.who.int/iris/bitstream/10665/178879/1/9789241565011_eng.pdf. Accessed on March 20th, 2016.
Njenga, F. (2002). 'Focus on Psychiatry in East Africa'. British Journal of Psychiatry (181): 354-59.
Idowu, P. A., Aladekomo, T. A., Williams, K. O. and Balogun, J. A. (2015). Predictive Model for Likelihood of Survival of Sickle Cell Anemia (SCA) among Pediatric Patients using Fuzzy Logic. Transactions in Networks and Communications 31 (1): 31-44.
Deziel, M., Olawo, D., Truchon, L. and Golab, L. (2013). Analyzing the Mental Health of Engineering Students Using Classification and Regression.
Kipli, K., Kouzani, Z. and Hamid, I. R. (2013). Investing Machine Learning Techniques for Detection of Depression Using Structural MRI Volumetric Features. International Journal of Biosciences, Biochemistry and Bioinformatics 3 (5): 444–448.
Seixas, F. L., Zadrozny, B., Laks, J., Conci, A., & Saade, D. C. M. (2014). A Bayesian network decision model for supporting the diagnosis of dementia, Alzheimer׳ s disease and mild cognitive impairment. Computers in biology and medicine, 51, 140-158.
Agbelusi, O. (2014). Development of a Predictive Model for Survival of HIV/AIDS Patients in South-Western Nigeria. Unpublished MPhil Thesis submitted to the Department of Computer Science and Engineering, Obafemi Awolowo University.
Bhakta, I and Sau, A. (2016). Prediction of Depression among Senior Citizens using Machine Learning Classifiers. International Journal of Computer Applications 144 (7): 11–16.
Sumanthi, M. R. and Pooma, B. (2016). Prediction of Mental Health Problems among Children Using Machine Learning Techniques. International Journal of Advanced Computer Science and Applications 7 (1): 552–557.