{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T16:36:14Z","timestamp":1781282174842,"version":"3.54.1"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2018,4,3]],"date-time":"2018-04-03T00:00:00Z","timestamp":1522713600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Med Syst"],"published-print":{"date-parts":[[2018,5]]},"DOI":"10.1007\/s10916-018-0934-5","type":"journal-article","created":{"date-parts":[[2018,4,3]],"date-time":"2018-04-03T02:05:25Z","timestamp":1522721125000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":206,"title":["Behavioral Modeling for Mental Health using Machine Learning Algorithms"],"prefix":"10.1007","volume":"42","author":[{"given":"M.","family":"Srividya","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S.","family":"Mohanavalli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"N.","family":"Bhalaji","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2018,4,3]]},"reference":[{"key":"934_CR1","unstructured":"Miner, L., et al., Practical predictive analytics and decisioning systems for medicine: Informatics accuracy and cost-effectiveness for healthcare administration and delivery including medical research. Cambridge: Academic Press, 2014."},{"key":"934_CR2","doi-asserted-by":"crossref","unstructured":"Luxton, D. D., (ed.) Artificial Intelligence in Behavioral and Mental Health Care. Amsterdam: Elsevier Inc., 2015.","DOI":"10.1016\/B978-0-12-420248-1.00001-5"},{"issue":"1","key":"934_CR3","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1038\/mp.2016.201","volume":"22","author":"T Hahn","year":"2017","unstructured":"Hahn, T., Nierenberg, A. A., and Whitfield-Gabrieli, S., Predictive analytics in mental health: applications, guidelines, challenges and perspectives. Mol. Psychiatry 22(1):37\u201343, 2017.","journal-title":"Mol. Psychiatry"},{"issue":"12","key":"934_CR4","doi-asserted-by":"publisher","first-page":"587","DOI":"10.1007\/s001270050098","volume":"33","author":"RV Bijl","year":"1998","unstructured":"Bijl, R. V., Ravelli, A., and Van Zessen, G., Prevalence of psychiatric disorder in the general population: results of The Netherlands Mental Health Survey and Incidence Study (NEMESIS). Soc. Psychiatry Psychiatr. Epidemiol. 33(12):587\u2013595, 1998.","journal-title":"Soc. Psychiatry Psychiatr. Epidemiol."},{"key":"934_CR5","volume-title":"Mental health: a call for action by world health ministers","author":"World Health Organization","year":"2001","unstructured":"World Health Organization, Mental health: a call for action by world health ministers. Geneva: World Health Organization, Department of Mental Health and Substance Dependence, 2001."},{"key":"934_CR6","unstructured":"Funk, M., Global burden of mental disorders and the need for a comprehensive, coordinated response from health and social sectors at the country level. http:\/\/apps.who.int\/gb\/ebwha\/pdf_files\/EB130\/B130_9-en.pdf . Accessed 20 Feb 2016, 2016."},{"key":"934_CR7","doi-asserted-by":"publisher","unstructured":"Drapeau, A., Marchand, A., and Beaulieu-Pr\u00e9vost, D. Mental illnesses-understanding, prediction and control. Epidemiol. Psychol. Distress, (2012). https:\/\/doi.org\/10.5772\/1235 .","DOI":"10.5772\/1235"},{"issue":"2","key":"934_CR8","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1007\/s007870050008","volume":"9","author":"R Goodman","year":"2000","unstructured":"Goodman, R., Renfrew, D., and Mullick, M., Predicting type of psychiatric disorder from Strengths and Difficulties Questionnaire (SDQ) scores in child mental health clinics in London and Dhaka. Eur. Child Adolesc. Psychiatry 9(2):129\u2013134, 2000.","journal-title":"Eur. Child Adolesc. Psychiatry"},{"issue":"1","key":"934_CR9","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1109\/JBHI.2014.2360711","volume":"19","author":"A Lanata","year":"2015","unstructured":"Lanata, A. et al., Complexity index from a personalized wearable monitoring system for assessing remission in mental health. IEEE J. Biomed. Health Inform. 19(1):132\u2013139, 2015.","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"2","key":"934_CR10","doi-asserted-by":"publisher","first-page":"212","DOI":"10.1037\/abn0000232","volume":"126","author":"JD Schaefer","year":"2017","unstructured":"Schaefer, J. D., et al. \"Enduring mental health: Prevalence and prediction.\". J. Abnorm. Psychol. 126(2):212, 2017.","journal-title":"J. Abnorm. Psychol."},{"key":"934_CR11","doi-asserted-by":"publisher","unstructured":"Qiu, T., Zhang, Y., Qiao, D., Zhang, X., Wymore, M. L., & Sangaiah, A. K., A robust time synchronization scheme for industrial internet of things. IEEE Trans. Ind. Inf., 2017. https:\/\/doi.org\/10.1109\/TII.2017.2738842 .","DOI":"10.1109\/TII.2017.2738842"},{"issue":"11","key":"934_CR12","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1109\/MCOM.2017.1700033","volume":"55","author":"T Qiu","year":"2017","unstructured":"Qiu, T., Qiao, R., Han, M., Sangaiah, A. K., and Lee, I., A Lifetime-Enhanced Data Collecting Scheme for the Internet of Things. IEEE Commun. Mag. 55(11):132\u2013137, 2017.","journal-title":"IEEE Commun. Mag."},{"key":"934_CR13","doi-asserted-by":"publisher","unstructured":"Kumar, P., Kumari, S., Sharma, V., Sangaiah, A. K., Wei, J., and Li, X., A Certificateless aggregate signature scheme for healthcare wireless sensor network. Sustain. Comput. Inf. Syst., 2017. https:\/\/doi.org\/10.1016\/j.suscom.2017.09.002 .","DOI":"10.1016\/j.suscom.2017.09.002"},{"key":"934_CR14","doi-asserted-by":"publisher","unstructured":"Sangaiah, A. K., Samuel, O. W., Li, X., Abdel-Basset, M., and Wang, H., Towards an efficient risk assessment in software projects\u2013Fuzzy reinforcement paradigm. Comput. Electr. Eng., 2017. https:\/\/doi.org\/10.1016\/j.compeleceng.2017.07.022 .","DOI":"10.1016\/j.compeleceng.2017.07.022"},{"key":"934_CR15","doi-asserted-by":"crossref","unstructured":"Wu, F., et al., A lightweight and robust two-factor authentication scheme for personalized healthcare systems using wireless medical sensor networks. Futur. Gener. Comput. Syst. 82:727\u2013737, 2017.","DOI":"10.1016\/j.future.2017.08.042"},{"key":"934_CR16","doi-asserted-by":"crossref","unstructured":"Aborokbah, M. M., Al-Mutairi, S., Sangaiah, A. K., and Samuel, O. W., Adaptive context aware decision computing paradigm for intensive health care delivery in smart cities\u2014A case analysis. Sustain. Cities Soc. 2017.","DOI":"10.1016\/j.scs.2017.09.004"},{"key":"934_CR17","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1016\/j.sbspro.2013.06.499","volume":"84","author":"M Chinaveh","year":"2013","unstructured":"Chinaveh, M., The effectiveness of problem-solving on coping skills and psychological adjustment. Procedia. Soc. Behav. Sci. 84:4\u20139, 2013.","journal-title":"Procedia. Soc. Behav. Sci."},{"key":"934_CR18","doi-asserted-by":"publisher","first-page":"549","DOI":"10.1016\/j.sbspro.2013.06.602","volume":"84","author":"A Hajiyakhchali","year":"2013","unstructured":"Hajiyakhchali, A., The Effects of Creative Problem Solving Process Training on Academic Well-being of Shahid Chamran University Students. Procedia. Soc. Behav. Sci. 84:549\u2013552, 2013.","journal-title":"Procedia. Soc. Behav. Sci."},{"key":"934_CR19","doi-asserted-by":"publisher","first-page":"569","DOI":"10.1016\/j.sbspro.2013.06.605","volume":"84","author":"A Aghaei","year":"2013","unstructured":"Aghaei, A., Khayyamnekouei, Z., and Yousefy, A., General health prediction based on life orientation, quality of life, life satisfaction and age. Procedia. Soc. Behav. Sci. 84:569\u2013573, 2013.","journal-title":"Procedia. Soc. Behav. Sci."},{"key":"934_CR20","doi-asserted-by":"crossref","unstructured":"Strauss, J., Peguero, A. M., and Hirst, G., Machine learning methods for clinical forms analysis in mental health. MedInfo. 192:1024, 2013.","DOI":"10.3233\/978-1-61499-289-9-1024"},{"issue":"9","key":"934_CR21","doi-asserted-by":"publisher","first-page":"11305","DOI":"10.1007\/s11042-016-3444-9","volume":"76","author":"Y Jung","year":"2017","unstructured":"Jung, Y., and Yoon, Y. I., Multi-level assessment model for wellness service based on human mental stress level. Multimedia Tools and Applications 76(9):11305\u201311317, 2017.","journal-title":"Multimedia Tools and Applications"},{"key":"934_CR22","doi-asserted-by":"crossref","unstructured":"Wang, H., and Wang, J., An effective image representation method using kernel classification. Tools with Artificial Intelligence (ICTAI), 2014 I.E. 26th International Conference on. IEEE, 2014.","DOI":"10.1109\/ICTAI.2014.131"},{"issue":"3","key":"934_CR23","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1093\/bioinformatics\/btw644","volume":"33","author":"X Cheng","year":"2016","unstructured":"Cheng, X. et al., iATC-mISF: a multi-label classifier for predicting the classes of anatomical therapeutic chemicals. Bioinformatics 33(3):341\u2013346, 2016.","journal-title":"Bioinformatics"},{"issue":"1","key":"934_CR24","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1176\/appi.ajp-rj.2017.120105","volume":"12","author":"G Rakesh","year":"2017","unstructured":"Rakesh, G., Suicide Prediction With Machine Learning. Am. J. Psychiatry Residents' J. 12(1):15\u201317, 2017.","journal-title":"Am. J. Psychiatry Residents' J."},{"issue":"9","key":"934_CR25","doi-asserted-by":"publisher","first-page":"2009","DOI":"10.1017\/S0033291716000611","volume":"46","author":"JD Ribeiro","year":"2016","unstructured":"Ribeiro, J. D. et al., Letter to the Editor: Suicide as a complex classification problem: machine learning and related techniques can advance suicide prediction-a reply to Roaldset (2016). Psychol. Med. 46(9):2009, 2016.","journal-title":"Psychol. Med."},{"issue":"10","key":"934_CR26","doi-asserted-by":"publisher","first-page":"1366","DOI":"10.1038\/mp.2015.198","volume":"21","author":"RC Kessler","year":"2016","unstructured":"Kessler, R. C., et al., Testing a machine-learning algorithm to predict the persistence and severity of major depressive disorder from baseline self-reports. Mol. Psychiatry. 21(10):1366, 2016.","journal-title":"Mol. Psychiatry."},{"issue":"2","key":"934_CR27","doi-asserted-by":"publisher","first-page":"274","DOI":"10.1109\/TITB.2009.2037317","volume":"14","author":"A Fleury","year":"2010","unstructured":"Fleury, A., Vacher, M., and Noury, N., SVM-based multimodal classification of activities of daily living in health smart homes: sensors, algorithms, and first experimental results. IEEE Trans. Inf. Technol. Biomed. 14(2):274\u2013283, 2010.","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"934_CR28","doi-asserted-by":"crossref","unstructured":"Smets, E., et al. Comparison of machine learning techniques for psychophysiological stress detection. International Symposium on Pervasive Computing Paradigms for Mental Health. Springer International Publishing, 2015.","DOI":"10.1007\/978-3-319-32270-4_2"},{"key":"934_CR29","doi-asserted-by":"crossref","unstructured":"Xu, J., et al. On the properties of mean opinion scores for quality of experience management. Multimedia (ISM), 2011 I.E. International Symposium on. IEEE, 2011.","DOI":"10.1109\/ISM.2011.88"},{"issue":"sup1","key":"934_CR30","doi-asserted-by":"publisher","first-page":"S44","DOI":"10.1080\/13102818.2014.949045","volume":"28","author":"YG Jung","year":"2014","unstructured":"Jung, Y. G., Kang, M. S., and Heo, J., Clustering performance comparison using K-means and expectation maximization algorithms. Biotechnol. Biotechnol. Equip. 28(sup1):S44\u2013S48, 2014.","journal-title":"Biotechnol. Biotechnol. Equip."},{"issue":"5","key":"934_CR31","doi-asserted-by":"publisher","first-page":"586","DOI":"10.1037\/pas0000201","volume":"28","author":"ML Kern","year":"2016","unstructured":"Kern, M. L., et al. \"The EPOCH Measure of Adolescent Well-Being.\". Psychol. Assess. 28(5):586, 2016.","journal-title":"Psychol. Assess."},{"issue":"4","key":"934_CR32","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1177\/014662168701100401","volume":"11","author":"GW Milligan","year":"1987","unstructured":"Milligan, G. W., and Cooper, M. C., Methodology review: Clustering methods. Appl. Psychol. Meas. 11(4):329\u2013354, 1987.","journal-title":"Appl. Psychol. Meas."},{"key":"934_CR33","doi-asserted-by":"crossref","unstructured":"Dziopa, T., Clustering Validity Indices Evaluation with Regard to Semantic Homogeneity. FedCSIS Position Papers 2016.","DOI":"10.15439\/2016F371"},{"key":"934_CR34","doi-asserted-by":"crossref","unstructured":"Aggarwal, C. C., and Zhai, C. X., A survey of text classification algorithms. Mining text data. Springer US, 163\u2013222, 2012.","DOI":"10.1007\/978-1-4614-3223-4_6"},{"issue":"2","key":"934_CR35","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1023\/A:1009715923555","volume":"2","author":"CJC Burges","year":"1998","unstructured":"Burges, C. J. C., A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Disc. 2(2):121\u2013167, 1998.","journal-title":"Data Min. Knowl. Disc."},{"issue":"3","key":"934_CR36","doi-asserted-by":"publisher","first-page":"440","DOI":"10.1162\/neco.1991.3.3.440","volume":"3","author":"Y Lee","year":"1991","unstructured":"Lee, Y., Handwritten digit recognition using k nearest-neighbor, radial-basis function, and backpropagation neural networks. Neural Comput. 3(3):440\u2013449, 1991.","journal-title":"Neural Comput."},{"issue":"1","key":"934_CR37","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1186\/1471-2105-9-319","volume":"9","author":"A Statnikov","year":"2008","unstructured":"Statnikov, A., Wang, L., and Aliferis, C. F., A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification. BMC Bioinformatics 9(1):319, 2008.","journal-title":"BMC Bioinformatics"},{"key":"934_CR38","doi-asserted-by":"crossref","unstructured":"Joachims, T., Text categorization with support vector machines: learning with many relevant features. In: European conference on machine learning. Pp. 137\u2013142. Berlin, Heidelberg: Springer, 1998.","DOI":"10.1007\/BFb0026683"},{"issue":"3","key":"934_CR39","doi-asserted-by":"publisher","first-page":"399","DOI":"10.1016\/S0034-4257(97)00049-7","volume":"61","author":"MA Friedl","year":"1997","unstructured":"Friedl, M. A., and Brodley, C. E., Decision tree classification of land cover from remotely sensed data. Remote Sens. Environ. 61(3):399\u2013409, 1997.","journal-title":"Remote Sens. Environ."},{"issue":"5","key":"934_CR40","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1155\/S1110724303210032","volume":"2003","author":"A Vlahou","year":"2003","unstructured":"Vlahou, A. et al., Diagnosis of ovarian cancer using decision tree classification of mass spectral data. Biomed. Res. Int. 2003(5):308\u2013314, 2003.","journal-title":"Biomed. Res. Int."},{"key":"934_CR41","doi-asserted-by":"publisher","first-page":"171","DOI":"10.2528\/PIER13121310","volume":"144","author":"Y Zhang","year":"2014","unstructured":"Zhang, Y., Wang, S., and Dong, Z., Classification of Alzheimer disease based on structural magnetic resonance imaging by kernel support vector machine decision tree. Prog. Electromagn. Res. 144:171\u2013184, 2014.","journal-title":"Prog. Electromagn. Res."},{"key":"934_CR42","doi-asserted-by":"crossref","unstructured":"Jiang, L., et al. Survey of improving k-nearest-neighbor for classification.\" Fuzzy Systems and Knowledge Discovery, 2007. FSKD 2007. Fourth International Conference on. Vol. 1. IEEE, 2007.","DOI":"10.1109\/FSKD.2007.552"},{"issue":"5","key":"934_CR43","doi-asserted-by":"publisher","first-page":"439","DOI":"10.1016\/S0167-4048(02)00514-X","volume":"21","author":"Y Liao","year":"2002","unstructured":"Liao, Y., and Rao Vemuri, V., Use of k-nearest neighbor classifier for intrusion detection. Comput Secur 21(5):439\u2013448, 2002.","journal-title":"Comput Secur"},{"key":"934_CR44","doi-asserted-by":"crossref","unstructured":"Liu, B., et al. Scalable sentiment classification for big data analysis using naive bayes classifier. Big Data, 2013 I.E. International Conference on. IEEE, 2013.","DOI":"10.1109\/BigData.2013.6691740"},{"issue":"5","key":"934_CR45","doi-asserted-by":"publisher","first-page":"352","DOI":"10.1016\/S1532-0464(03)00034-0","volume":"35","author":"S Dreiseitl","year":"2002","unstructured":"Dreiseitl, S., and Ohno-Machado, L., Logistic regression and artificial neural network classification models: a methodology review. J. Biomed. Inform. 35(5):352\u2013359, 2002.","journal-title":"J. Biomed. Inform."},{"key":"934_CR46","doi-asserted-by":"crossref","unstructured":"Ribeiro, M. T., Singh, S., and Guestrin, C., Why should i trust you?: Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2016.","DOI":"10.1145\/2939672.2939778"},{"key":"934_CR47","doi-asserted-by":"crossref","unstructured":"Kuncheva, L. I. Combining pattern classifiers: methods and algorithms. New York: John Wiley & Sons, 2004.","DOI":"10.1002\/0471660264"},{"issue":"1","key":"934_CR48","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L., Random forests. Mach. Learn. 45(1):5\u201332, 2001.","journal-title":"Mach. Learn."},{"issue":"4","key":"934_CR49","doi-asserted-by":"publisher","first-page":"294","DOI":"10.1016\/j.patrec.2005.08.011","volume":"27","author":"PO Gislason","year":"2006","unstructured":"Gislason, P. O., Benediktsson, J. A., and Sveinsson, J. R., Random forests for land cover classification. Pattern Recogn. Lett. 27(4):294\u2013300, 2006.","journal-title":"Pattern Recogn. Lett."}],"container-title":["Journal of Medical Systems"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10916-018-0934-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10916-018-0934-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10916-018-0934-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,3]],"date-time":"2025-07-03T10:36:59Z","timestamp":1751539019000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10916-018-0934-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,4,3]]},"references-count":49,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2018,5]]}},"alternative-id":["934"],"URL":"https:\/\/doi.org\/10.1007\/s10916-018-0934-5","relation":{},"ISSN":["0148-5598","1573-689X"],"issn-type":[{"value":"0148-5598","type":"print"},{"value":"1573-689X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,4,3]]},"assertion":[{"value":"24 July 2017","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 March 2018","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 April 2018","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"\u201cAll procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and\/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.\u201d","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"\u201cInformed consent was obtained from all individual participants included in the study.\u201d","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}],"article-number":"88"}}