A Predictive Model for the Risk of Mental Illness in Nigeria Using Data Mining

Mhambe Priscilla Dooshima; Egejuru Ngozi Chidozie; Balogun Jeremiah Ademola; Olusanya Olayinka Sekoni; Idowu Peter Adebayo

doi:doi:10.11648/j.iji.20180601.12

A Predictive Model for the Risk of Mental Illness in Nigeria Using Data Mining

Mhambe Priscilla Dooshima, Egejuru Ngozi Chidozie, Balogun Jeremiah Ademola, Olusanya Olayinka Sekoni, Idowu Peter Adebayo

Published in International Journal of Immunology (Volume 6, Issue 1)

Received: 24 October 2017 Accepted: 9 November 2017 Published: 23 January 2018

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Abstract

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.

Published in	International Journal of Immunology (Volume 6, Issue 1)
DOI	10.11648/j.iji.20180601.12
Page(s)	5-16
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2018. Published by Science Publishing Group

Keywords

Mental Illness, Predictive Modeling, Machine Learning, Risk Classification

References

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[7]	Hanlon, C., Wondimagegn, D. and Alem, A. (2010). Lessons Learned in Developing Community Mental Health Care in Africa. World Psychiatry 9 (3): 185–189.
[8]	World Health Organization (2011b). WHO African Regional Ministerial Consultation on Non-communicable Diseases. Brazzaville, Congo. Brazzaville: WHO Regional Office for Africa.
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[10]	Njenga, F. (2002). 'Focus on Psychiatry in East Africa'. British Journal of Psychiatry (181): 354-59.
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[12]	Deziel, M., Olawo, D., Truchon, L. and Golab, L. (2013). Analyzing the Mental Health of Engineering Students Using Classification and Regression.
[13]	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.
[14]	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.
[15]	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.
[16]	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.
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APA Style

Mhambe Priscilla Dooshima, Egejuru Ngozi Chidozie, Balogun Jeremiah Ademola, Olusanya Olayinka Sekoni, Idowu Peter Adebayo. (2018). A Predictive Model for the Risk of Mental Illness in Nigeria Using Data Mining. International Journal of Immunology, 6(1), 5-16. https://doi.org/10.11648/j.iji.20180601.12

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ACS Style

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. Int. J. Immunol. 2018, 6(1), 5-16. doi: 10.11648/j.iji.20180601.12

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AMA Style

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. Int J Immunol. 2018;6(1):5-16. doi: 10.11648/j.iji.20180601.12

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@article{10.11648/j.iji.20180601.12,
  author = {Mhambe Priscilla Dooshima and Egejuru Ngozi Chidozie and Balogun Jeremiah Ademola and Olusanya Olayinka Sekoni and Idowu Peter Adebayo},
  title = {A Predictive Model for the Risk of Mental Illness in Nigeria Using Data Mining},
  journal = {International Journal of Immunology},
  volume = {6},
  number = {1},
  pages = {5-16},
  doi = {10.11648/j.iji.20180601.12},
  url = {https://doi.org/10.11648/j.iji.20180601.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.iji.20180601.12},
  abstract = {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.},
 year = {2018}
}

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TY  - JOUR
T1  - A Predictive Model for the Risk of Mental Illness in Nigeria Using Data Mining
AU  - Mhambe Priscilla Dooshima
AU  - Egejuru Ngozi Chidozie
AU  - Balogun Jeremiah Ademola
AU  - Olusanya Olayinka Sekoni
AU  - Idowu Peter Adebayo
Y1  - 2018/01/23
PY  - 2018
N1  - https://doi.org/10.11648/j.iji.20180601.12
DO  - 10.11648/j.iji.20180601.12
T2  - International Journal of Immunology
JF  - International Journal of Immunology
JO  - International Journal of Immunology
SP  - 5
EP  - 16
PB  - Science Publishing Group
SN  - 2329-1753
UR  - https://doi.org/10.11648/j.iji.20180601.12
AB  - 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.
VL  - 6
IS  - 1
ER  -

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Author Information

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

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APA Style

Mhambe Priscilla Dooshima, Egejuru Ngozi Chidozie, Balogun Jeremiah Ademola, Olusanya Olayinka Sekoni, Idowu Peter Adebayo. (2018). A Predictive Model for the Risk of Mental Illness in Nigeria Using Data Mining. International Journal of Immunology, 6(1), 5-16. https://doi.org/10.11648/j.iji.20180601.12

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ACS Style

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. Int. J. Immunol. 2018, 6(1), 5-16. doi: 10.11648/j.iji.20180601.12

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AMA Style

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. Int J Immunol. 2018;6(1):5-16. doi: 10.11648/j.iji.20180601.12

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@article{10.11648/j.iji.20180601.12,
  author = {Mhambe Priscilla Dooshima and Egejuru Ngozi Chidozie and Balogun Jeremiah Ademola and Olusanya Olayinka Sekoni and Idowu Peter Adebayo},
  title = {A Predictive Model for the Risk of Mental Illness in Nigeria Using Data Mining},
  journal = {International Journal of Immunology},
  volume = {6},
  number = {1},
  pages = {5-16},
  doi = {10.11648/j.iji.20180601.12},
  url = {https://doi.org/10.11648/j.iji.20180601.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.iji.20180601.12},
  abstract = {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.},
 year = {2018}
}

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TY  - JOUR
T1  - A Predictive Model for the Risk of Mental Illness in Nigeria Using Data Mining
AU  - Mhambe Priscilla Dooshima
AU  - Egejuru Ngozi Chidozie
AU  - Balogun Jeremiah Ademola
AU  - Olusanya Olayinka Sekoni
AU  - Idowu Peter Adebayo
Y1  - 2018/01/23
PY  - 2018
N1  - https://doi.org/10.11648/j.iji.20180601.12
DO  - 10.11648/j.iji.20180601.12
T2  - International Journal of Immunology
JF  - International Journal of Immunology
JO  - International Journal of Immunology
SP  - 5
EP  - 16
PB  - Science Publishing Group
SN  - 2329-1753
UR  - https://doi.org/10.11648/j.iji.20180601.12
AB  - 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.
VL  - 6
IS  - 1
ER  -

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