Effective Classification of Major Depressive Disorder Patients Using Machine Learning Techniques

Author(s): Nivedhitha Mahendran, Durai Raj Vincent*.

Journal Name: Recent Patents on Computer Science

Volume 12 , Issue 1 , 2019

Submit Manuscript
Submit Proposal

Graphical Abstract:


Abstract:

Background: Major Depressive Disorder (MDD) in simple terms is a psychiatric disorder which may be indicated by having mood disturbances which are consistent for more than a few weeks. It is considered a serious threat to psychophysiology which when left undiagnosed may even lead to the death of the victim so it is more important to have an effective predictive model. The major Depressive disorder is often termed as comorbid medical condition (medical condition that co-occurs with another), it is hardly possible for the physicians to predict that the victim is under depression, timely diagnosis of MDD may help in avoiding other comorbidities. Machine learning is a branch of artificial intelligence which makes the system capable of learning from the past and with that experience improves the future results even without programming explicitly. As in recent days because of the high dimensionality of features, the accuracy of the predictions is comparatively low. In order to get rid of redundant and unrelated features from the data and improve the accuracy, relevant features must be selected using effective feature selection methods.

Objective: This study aims to develop a predictive model for diagnosing the Major Depressive Disorder among the IT professionals by reducing the feature dimension using feature selection techniques and evaluate them by implementing three machine learning classifiers such as Naïve Bayes, Support Vector Machines and Decision Tree.

Method: We have used Random Forest based Recursive Feature Elimination technique to reduce the feature dimensions.

Results: The results show a considerable increase in prediction accuracy after applying feature selection technique.

Conclusion: From the results, it is implied that the classification algorithms perform better after reducing the feature dimensions.

Keywords: Major Depressive Disorder (MDD), feature selection, Correlation-based Feature Selection (CFS), Random Forest based Reverse Feature Elimination (RT-RFE), naïve bayes, Support Vector Machines (SVM), Decision Tree (DT).

[1]
M.M. Gerrits, P. van Oppen, H.W. van Marwijk, B.W. Penninx, and H.E. van der Horst, "Pain and the onset of depressive and anxiety disorders", Pain, vol. 155, pp. 53-59, 2014.
[2]
N. Kabra, and A. Nadkarni, "Prevalence of depression and anxiety in irritable bowel syndrome: A clinic based study from India", Indian J. Psychiatry, vol. 55, p. 77, 2013.
[3]
G.I. Papakostas, T. Petersen, Y. Mahal, D. Mischoulon, A.A. Nierenberg, and M. Fava, "Quality of life assessments in major depressive disorder: A review of the literature", Gen. Hosp. Psychiatry, vol. 26, pp. 13-17, 2004.
[4]
A. Karasz, C. Dowrick, R. Byng, M. Buszewicz, L. Ferri, T.C.O. Hartman, and J. Reeve, "What we talk about when we talk about depression: Doctor-patient conversations and treatment decision outcomes", Br. J. Gen. Pract., vol. 62, pp. e55-e63, 2012.
[5]
M.D. Feldman, P. Franks, P.R. Duberstein, S. Vannoy, R. Epstein, and R.L. Kravitz, "Let’s not talk about it: Suicide inquiry in primary care", Ann. Fam. Med., vol. 5, pp. 412-418, 2007.
[6]
Z.M. Hira, and D.F. Gillies, "A review of feature selection and feature extraction methods applied on microarray data", Adv. Bioinforma., vol. 2015, p. 198363, 2015.
[7]
"T. Hamed, R. Dara and S. C. Kremer, “An accurate, fast embedded feature selection for SVMs”, In", 13th International Conference on Machine Learning and Applications (ICMLA),. pp. 135-140, 2014.
[8]
M. Milanovic, K. Holshausen, R. Milev, and C.R. Bowie, "Functional competence in major depressive disorder: Objective performance and subjective perceptions", J. Affect. Disord., vol. 234, pp. 1-7, 2018.
[9]
J. Wee, S. Jang, J. Lee, and W. Jang, "The influence of depression and personality on social networking", Comput. Human Behav., vol. 74, pp. 45-52, 2017.
[10]
A.G. Reece, and C.M. Danforth, "Instagram photos reveal predictive markers of depression", EPJ Data Sci., vol. 6, p. 15, 2017.
[11]
T. Mogi, H. Toda, and A. Yoshino, "Clinical characteristics of patients with diagnostic uncertainty of major depressive disorder", Asian J. Psychiatr., vol. 30, pp. 159-162, 2017.
[12]
J. Kim, T. Nakamura, H. Kikuchi, K. Yoshiuchi, T. Sasaki, and Y. Yamamoto, "Covariation of depressive mood and spontaneous physical activity in major depressive disorder: toward continuous monitoring of depressive mood", IEEE J. Biomed. Health Inform., vol. 19, pp. 1347-1355, 2015.
[13]
N.F. Jie, M.H. Zhu, X.Y. Ma, E.A. Osuch, M. Wammes, J. Théberge, and V.D. Calhoun, "Discriminating bipolar disorder from major depression based on SVM-FoBa: Efficient feature selection with multimodal brain imaging data", IEEE Trans. Auton. Ment. Dev., vol. 7, pp. 320-331, 2015.
[14]
"A. Esposito, F. Scibelli and A. Vinciarelli, A pilot study on the decoding of dynamic emotional expressions in major depressive disorder. In", Advances in Neural Networks,. pp. 189-200, Springer, Switzerland, 2016.
[15]
A. Sau, and I. Bhakta, "Predicting anxiety and depression in elderly patients using machine learning technology", Healthc. Technol. Lett., vol. 4, pp. 238-243, 2017.
[16]
I. Guyon, "J. Weston, S. Barnhill and V. Vapnik, “Gene selection for cancer classification using support vector machines", Mach. Learn., vol. 46, pp. 389-422, 2002.
[17]
Q. Zhou, H. Zhou, Q. Zhou, F. Yang, and L. Luo, "Structure damage detection based on random forest recursive feature elimination", Mech. Syst. Signal Process., vol. 46, pp. 82-90, 2014.
[18]
M. Mursalin, Y. Zhang, Y. Chen, and N.V. Chawla, "Automated epileptic seizure detection using improved correlation-based feature selection with random forest classifier", Neurocomputing, vol. 241, pp. 204-214, 2017.
[19]
"G. Manikandan, E. Susi and S. Abirami, “Feature Selection On High Dimensional Data Using Wrapper Based Subset Selection”, In", Second International Conference on Recent Trends and Challenges in Computational Models (ICRTCCM),. pp. 320-325, 2017.
[20]
P.M. Granitto, C. Furlanello, F. Biasioli, and F. Gasperi, "Recursive feature elimination with random forest for PTR-MS analysis of agroindustrial products", Chemom. Intell. Lab. Syst., vol. 83, pp. 83-90, 2006.
[21]
"I. Gad and B. R. Manjunatha, “Performance evaluation of predictive models for missing data imputation in weather data”, In", International Conference on Advances in Computing, Communications and Informatics (ICACCI),. pp. 1327-1334, 2017.
[22]
J.S. Shah, S.N. Rai, A.P. De Filippis, B.G. Hill, A. Bhatnagar, and G.N. Brock, "Distribution based nearest neighbor imputation for truncated high dimensional data with applications to pre-clinical and clinical metabolomics studies", BMC Bioinformatics, vol. 18, p. 114, 2017.
[23]
M.A. Hall, Correlation-based feature selection of discrete and numeric class machine learning., Hamilton, New Zealand, 2000.
[24]
H. Zhang, Z.X. Cao, M. Li, Y.Z. Li, and C. Peng, "Novel naive Bayes classification models for predicting the carcinogenicity of chemicals", Food Chem. Toxicol., vol. 97, pp. 141-149, 2016.
[25]
"Y. Hou, J. Xu, Y. Huang X. Ma, “A big data application to predict depression in the university based on the reading habits”, In", 3rd International Conference on Systems and Informatics (ICSAI),. pp. 1085-1089, 2016.
[26]
"O. Maimon and A. Browarnik, “NHECD-Nano health and environmental commented database”, In", Data Mining and Knowledge Discovery Handbook,. pp. 1221-1241, Springer, Boston, 2009.
[27]
R. Ramasubbu, M.R. Brown, F. Cortese, I. Gaxiola, B. Goodyear, A.J. Greenshaw, and R. Greiner, "Accuracy of automated classification of major depressive disorder as a function of symptom severity", Neuroimage Clin., vol. 12, pp. 320-331, 2016.
[28]
"B. Hosseinifard, M. H. Moradi R. Rostami, “Classifying depression patients and normal subjects using machine learning techniques”, In", 19th Iranian Conference on Electrical Engineering (ICEE),. pp. 1-4, 2011.
[29]
"L. Rokach and O. Z. Maimon,", Data mining with decision trees: Theory and applications,. World Scientific Publishing Company, Singapore, Vol. 69, 2008.
[30]
M. Sokolova, and G. Lapalme, "A systematic analysis of performance measures for classification tasks", Inf. Process. Manage., vol. 45, pp. 427-437, 2009.


Rights & PermissionsPrintExport Cite as


Article Details

VOLUME: 12
ISSUE: 1
Year: 2019
Page: [41 - 48]
Pages: 8
DOI: 10.2174/2213275911666181016160920
Price: $58

Article Metrics

PDF: 10
HTML: 1