[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.