Depression is a serious illness that affects millions each year and if left untreated, it may lead to the deaths of many. It comes in many flavors that can be very different among people and this makes diagnosing it very difficult. A lot of artificial intelligence systems have been designed to diagnosing depression but they failed to perform feature selection and extraction on the dataset used in training the systems and this has a huge implication on the classification accuracy of the system. The objective of this research work is to develop a depression diagnosis system, that takes into consideration feature selection and extraction of dataset using Genetic-Neuro-Fuzzy techniques. Feature selection and extraction, will enable identification of key symptoms and hidden traits which are vital in diagnosis of depression. In this work, a Genetic Neuro-Fuzzy Model which is capable of handling feature selection and extraction on depression dataset was proposed and designed for diagnosing clinical depression. The GA component optimizes the clinical dataset which consist of series of diagnosed depression cases by performing feature selection and extraction, while ANFIS is used in training the optimized dataset obtained from the GA. The system had 92.5% prediction accuracy. This is a significant improvement over the best related model in literature that achieved a prediction accuracy of 92.4%. The system is recommended for psychiatrist hospital to aid in depression diagnosis. The research is limited to the diagnosis of clinical depression; future work should focus on the other forms of depression and treatments. The model has incorporated feature selection and feature extraction for the prediction of clinical depression with significant results established with performance indicators.
| Published in | Machine Learning Research (Volume 6, Issue 2) |
| DOI | 10.11648/j.mlr.20210602.12 |
| Page(s) | 17-23 |
| 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), 2024. Published by Science Publishing Group |
Depression, Genetic Algorithm, Neural Networks
| [1] | DSM V, Diagnostic and Statistical Manual for Mental Disorder Fifth Edition (DSM-V) Psychiatric Association Washington, DC, 2010. |
| [2] | S. Stanislava and D. Stoyanova, Depression Factors Symptoms, Prevention and the Role of open journal of depression Vol. 3, No. 1, 3-4., 2014. |
| [3] | Q. Feng, G. Frances, P. Nick, and J. Gunn, An exploratory statistical approach to depression pattern identification. Physical A 392 Pp. 889–901. www.elsevier.com/locate/physa. (Accessed July 12, 2017), 2013. |
| [4] | W. Jinghui, W. Xiaohang, L. Weiyi, L. Erping, Z. Xiayin, L. Wangting, Z. Yi, C. Chuan, Z. Xiaojian, L. Zhenzhen, W. Dongni, and L. Haotian, Prevalence of depression and depressive symptoms among outpatients: a systematic review and meta-analysis BMJ ; 7-8, 2017. |
| [5] | D. Pilgrim, The survival of psychiatric diagnosis, Social Science & Medicine 65 Pp. 536 547, 2007. |
| [6] | S. Chattopadhyay, P. Kaur, F. Rabhi, and R. Acharya, An automated system to diagnose the severity of adult depression, Proc. of 2nd IEEE Int. Conf. on Emerging Applications of Information technology. Pp. 121-124. DOI: 10.1109/EAIT.2011.17., 2012. |
| [7] | WHO: World Health Organization, Depression Fact Sheet, 2014. |
| [8] | WHO: World Health Organization, Depression Fact Sheet, 2016. |
| [9] | WHO: World Health Organization, Depression Fact Sheet, 2017. |
| [10] | N. Neil and R. Francisco, Nedley Depression Hit Hypothesis: Identify Depression and its causes. American Journal of Life Style Medicine Pp. 423-428, 2016. |
| [11] | D. Anish, B. Nirman, and C. Subhagata, ANeuro-Fuzzy System for Modeling the Depression Data. International Journal of Computer Application. Volume 54, No 6. Pp. 1-6., 2012. |
| [12] | V. E. Ekong, Ekong, UO, Uwadiae E, Abasiubong, F and Onibere, E. A., A Fuzzy Inference System for Predicting Depression Risk Levels, African Journal of Mathematics and Computer Research, Vol. 6, No. 10. Pp. 197-204., 2013. |
| [13] | V. E. Ekong, U. G Inyang, and E. A. Onibere, Intelligent DSS for depression diagnosis based on NF-CBR Hybrid, International Journal of Modern Applied Sciences, Vol. 6, No. 7. Pp. 79-88., 2012. |
| [14] | V. E. Ekong and E. A. Onibere, A Soft Computing Model for Depression Prediction. Egyptian Computer Science Journal Vol. 39 No. 4 Pp 1-21., 2015. |
| [15] | G. S. Alexopoulos, R. C. Abrams, R. C Young, and C. A. Shamoian, Cornell Scale for Depression in Dementia. Biol Psychiatry 23, 271-284., 1988. |
| [16] | L. S. Radloff, The CES-D scale: A self-report depression scale for research in the general population. Appl Psychological Measurement 1, 385-401., 1977. |
| [17] | L. K. Sharp, and M. S. Lipsky, Screening for Depression across the lifespan: A review of measures for use in primary care settings. Am. Fam. Physician 66 -6: 1001-1008., 2002. |
| [18] | J. I. Sheikh, and J. A. Yesavage, Geriatric depression scale (GDS): Recent evidence and development of a shorter version. Clinical gerontology: pp 165-173., 1986. |
| [19] | J. A. Yesavage, T. L Brink, T. L. Rose, O. Lum, V. Huang, M. B. Adey, and V. O. Leirer, Development and validation of a geriatric depression screening scale: A preliminary report. J Psychiatr Res 17, 37-49., 1983. |
| [20] | W. W. Zung, A self-rating depression scale. Arch Gen Psychiatry Vol. 12, pp 63-70, 1965. |
| [21] | V. I. Osubor and A. O. Egwali A neuro fuzzy Approach for the diagnosis of postpartum depression disorder, Iranian Journal of Computer Science 5-6, 56: 61. Doi: 10.1007/s42044-018-0021-6, 2018. |
| [22] | S. Arkaprabha and B. Ishita, Artificial Neural Network (ANN) Model to Predict Depression among Geriatric Population at a Slum in Kolkata, India. J Clin Diagn Res. 11-5: VC01–VC04., 2017. |
| [23] | S. Yoshihiko, X. Yinzhan, and S. P. Alex, DeepMood: Forecasting Depressed Mood Based on Self Reported Histories via Recurrent Neural Networks ACM. 978-1-4503-4913 2017, 2017. |
APA Style
Adegboyega Adegboye, Imianvan Anthony Agboizebeta. (2021). A Genetic Neuro-Fuzzy System for Diagnosing Clinical Depression. Machine Learning Research, 6(2), 17-23. https://doi.org/10.11648/j.mlr.20210602.12
ACS Style
Adegboyega Adegboye; Imianvan Anthony Agboizebeta. A Genetic Neuro-Fuzzy System for Diagnosing Clinical Depression. Mach. Learn. Res. 2021, 6(2), 17-23. doi: 10.11648/j.mlr.20210602.12
AMA Style
Adegboyega Adegboye, Imianvan Anthony Agboizebeta. A Genetic Neuro-Fuzzy System for Diagnosing Clinical Depression. Mach Learn Res. 2021;6(2):17-23. doi: 10.11648/j.mlr.20210602.12
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Download@article{10.11648/j.mlr.20210602.12,
author = {Adegboyega Adegboye and Imianvan Anthony Agboizebeta},
title = {A Genetic Neuro-Fuzzy System for Diagnosing Clinical Depression},
journal = {Machine Learning Research},
volume = {6},
number = {2},
pages = {17-23},
doi = {10.11648/j.mlr.20210602.12},
url = {https://doi.org/10.11648/j.mlr.20210602.12},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mlr.20210602.12},
abstract = {Depression is a serious illness that affects millions each year and if left untreated, it may lead to the deaths of many. It comes in many flavors that can be very different among people and this makes diagnosing it very difficult. A lot of artificial intelligence systems have been designed to diagnosing depression but they failed to perform feature selection and extraction on the dataset used in training the systems and this has a huge implication on the classification accuracy of the system. The objective of this research work is to develop a depression diagnosis system, that takes into consideration feature selection and extraction of dataset using Genetic-Neuro-Fuzzy techniques. Feature selection and extraction, will enable identification of key symptoms and hidden traits which are vital in diagnosis of depression. In this work, a Genetic Neuro-Fuzzy Model which is capable of handling feature selection and extraction on depression dataset was proposed and designed for diagnosing clinical depression. The GA component optimizes the clinical dataset which consist of series of diagnosed depression cases by performing feature selection and extraction, while ANFIS is used in training the optimized dataset obtained from the GA. The system had 92.5% prediction accuracy. This is a significant improvement over the best related model in literature that achieved a prediction accuracy of 92.4%. The system is recommended for psychiatrist hospital to aid in depression diagnosis. The research is limited to the diagnosis of clinical depression; future work should focus on the other forms of depression and treatments. The model has incorporated feature selection and feature extraction for the prediction of clinical depression with significant results established with performance indicators.},
year = {2021}
}
TY - JOUR T1 - A Genetic Neuro-Fuzzy System for Diagnosing Clinical Depression AU - Adegboyega Adegboye AU - Imianvan Anthony Agboizebeta Y1 - 2021/11/23 PY - 2021 N1 - https://doi.org/10.11648/j.mlr.20210602.12 DO - 10.11648/j.mlr.20210602.12 T2 - Machine Learning Research JF - Machine Learning Research JO - Machine Learning Research SP - 17 EP - 23 PB - Science Publishing Group SN - 2637-5680 UR - https://doi.org/10.11648/j.mlr.20210602.12 AB - Depression is a serious illness that affects millions each year and if left untreated, it may lead to the deaths of many. It comes in many flavors that can be very different among people and this makes diagnosing it very difficult. A lot of artificial intelligence systems have been designed to diagnosing depression but they failed to perform feature selection and extraction on the dataset used in training the systems and this has a huge implication on the classification accuracy of the system. The objective of this research work is to develop a depression diagnosis system, that takes into consideration feature selection and extraction of dataset using Genetic-Neuro-Fuzzy techniques. Feature selection and extraction, will enable identification of key symptoms and hidden traits which are vital in diagnosis of depression. In this work, a Genetic Neuro-Fuzzy Model which is capable of handling feature selection and extraction on depression dataset was proposed and designed for diagnosing clinical depression. The GA component optimizes the clinical dataset which consist of series of diagnosed depression cases by performing feature selection and extraction, while ANFIS is used in training the optimized dataset obtained from the GA. The system had 92.5% prediction accuracy. This is a significant improvement over the best related model in literature that achieved a prediction accuracy of 92.4%. The system is recommended for psychiatrist hospital to aid in depression diagnosis. The research is limited to the diagnosis of clinical depression; future work should focus on the other forms of depression and treatments. The model has incorporated feature selection and feature extraction for the prediction of clinical depression with significant results established with performance indicators. VL - 6 IS - 2 ER -
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