Volume 6, Issue 1, March 2018, Page: 5-16
A Predictive Model for the Risk of Mental Illness in Nigeria Using Data Mining
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
Received: Oct. 24, 2017;       Accepted: Nov. 9, 2017;       Published: Jan. 23, 2018
DOI: 10.11648/j.iji.20180601.12      View  1784      Downloads  88
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.
Mental Illness, Predictive Modeling, Machine Learning, Risk Classification
To cite this article
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. doi: 10.11648/j.iji.20180601.12
Copyright © 2018 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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