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.
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.
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.
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.
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.
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.
"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.
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.
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.
A.G. Reece, and C.M. Danforth, "Instagram photos reveal predictive markers of depression", EPJ Data Sci., vol. 6, p. 15, 2017.
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.
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.
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.
"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,
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.
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.
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.
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.
"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.
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.
"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.
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.
M.A. Hall, Correlation-based feature selection of discrete and numeric class machine learning., Hamilton, New Zealand, 2000.
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.
"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.
"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.
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.
"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.
"L. Rokach and O. Z. Maimon,", Data mining with decision trees:
Theory and applications,. World Scientific Publishing Company,
Singapore, Vol. 69, 2008.
M. Sokolova, and G. Lapalme, "A systematic analysis of performance measures for classification tasks", Inf. Process. Manage., vol. 45, pp. 427-437, 2009.