{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T15:11:17Z","timestamp":1780758677878,"version":"3.54.1"},"reference-count":63,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,3,25]],"date-time":"2022-03-25T00:00:00Z","timestamp":1648166400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Health Regional Service","award":["GRS 1801\/A\/18"],"award-info":[{"award-number":["GRS 1801\/A\/18"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>New computational methods have emerged through science and technology to support the diagnosis of mental health disorders. Predictive models developed from machine learning algorithms can identify disorders such as schizophrenia and support clinical decision making. This research aims to compare the performance of machine learning algorithms: Decision Tree, AdaBoost, Random Forest, Na\u00efve Bayes, Support Vector Machine, and k-Nearest Neighbor in the prediction of hospitalized patients with schizophrenia. The data set used in the study contains a total of 11,884 electronic admission records corresponding to 6933 patients with various mental health disorders; these records belong to the acute units of 11 public hospitals in a region of Spain. Of the total, 5968 records correspond to patients diagnosed with schizophrenia (3002 patients) and 5916 records correspond to patients with other mental health disorders (3931 patients). The results recommend Random Forest with the best accuracy of 72.7%. Furthermore, this algorithm presents 79.6%, 72.8%, 72.7%, and 72.7% for AUC, precision, F1-Score, and recall, respectively. The results obtained suggest that the use of machine learning algorithms can classify hospitalized patients with schizophrenia in this population and help in the hospital management of this type of disorder, to reduce the costs associated with hospitalization.<\/jats:p>","DOI":"10.3390\/s22072517","type":"journal-article","created":{"date-parts":[[2022,3,27]],"date-time":"2022-03-27T21:31:25Z","timestamp":1648416685000},"page":"2517","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Comparison of Machine Learning Algorithms in the Prediction of Hospitalized Patients with Schizophrenia"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3500-4100","authenticated-orcid":false,"given":"Susel","family":"G\u00f3ngora Alonso","sequence":"first","affiliation":[{"name":"Department of Signal Theory and Communications, and Telematics Engineering, University of Valladolid, Paseo de Bel\u00e9n, 15, 47011 Valladolid, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5834-6571","authenticated-orcid":false,"given":"Gon\u00e7alo","family":"Marques","sequence":"additional","affiliation":[{"name":"Polytechnic of Coimbra, ESTGOH, Rua General Santos Costa, 3400-124 Oliveira do Hospital, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Deevyankar","family":"Agarwal","sequence":"additional","affiliation":[{"name":"EEE Section, Department of Engineering, Higher College of Technology, Muscat 113, Oman"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3134-7720","authenticated-orcid":false,"given":"Isabel","family":"De la Torre D\u00edez","sequence":"additional","affiliation":[{"name":"Department of Signal Theory and Communications, and Telematics Engineering, University of Valladolid, Paseo de Bel\u00e9n, 15, 47011 Valladolid, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3639-2523","authenticated-orcid":false,"given":"Manuel","family":"Franco-Mart\u00edn","sequence":"additional","affiliation":[{"name":"Psiquiatry Service, Hospital Zamora, 49021 Zamora, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Pachange, S., Joglekar, B., and Kulkarni, P. (2015, January 17\u201320). An ensemble classifier approach for disease diagnosis using Random Forest. Proceedings of the 2015 Annual IEEE India Conference (INDICON), New Delhi, India.","DOI":"10.1109\/INDICON.2015.7443826"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"157614","DOI":"10.1109\/ACCESS.2019.2950030","article-title":"Applying Machine Learning to Identify Autism with Restricted Kinematic Features","volume":"7","author":"Zhao","year":"2019","journal-title":"IEEE Access"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"e17364","DOI":"10.2196\/17364","article-title":"Predicting Breast Cancer in Chinese Women Using Machine Learning Techniques: Algorithm Development","volume":"8","author":"Hou","year":"2020","journal-title":"JMIR Med. Inform."},{"key":"ref_4","first-page":"71","article-title":"Using a Data Mining Approach to Discover Behavior Correlates of Chronic Disease: A Case Study of Depression","volume":"201","author":"Yoon","year":"2014","journal-title":"Stud. Health Technol. Inform."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.ijmedinf.2017.10.002","article-title":"Early hospital mortality prediction of intensive care unit patients using an ensemble learning approach","volume":"108","author":"Awad","year":"2017","journal-title":"Int. J. Med. Inform."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"101704","DOI":"10.1016\/j.artmed.2019.101704","article-title":"Machine learning and big data: Implications for disease modeling and therapeutic discovery in psychiatry","volume":"99","author":"Tai","year":"2019","journal-title":"Artif. Intell. Med."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Dhaka, P., and Johari, R. (2016, January 3\u20135). Big data application: Study and archival of mental health data, using MongoDB. Proceedings of the 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), Chennai, India.","DOI":"10.1109\/ICEEOT.2016.7755300"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"103973","DOI":"10.1016\/j.ijmedinf.2019.103973","article-title":"Individualized prediction of depressive disorder in the elderly: A multitask deep learning approach","volume":"132","author":"Xu","year":"2019","journal-title":"Int. J. Med. Inform."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1195","DOI":"10.1093\/schbul\/sby058","article-title":"Global Epidemiology and Burden of Schizophrenia: Findings from the Global Burden of Disease Study 2016","volume":"44","author":"Charlson","year":"2018","journal-title":"Schizophr. Bull."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Orrico-S\u00e1nchez, A., L\u00f3pez-Lacort, M., Mu\u00f1oz-Quiles, C., Sanf\u00e9lix-Gimeno, G., and D\u00edez-Domingo, J. (2020). Epidemiology of schizophrenia and its management over 8-years period using real-world data in Spain. BMC Psychiatry, 20.","DOI":"10.1186\/s12888-020-02538-8"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"American Psychiatric Association (2013). Diagnostic and Statistical Manual of Mental Disorders, Elsevier. [5th ed.].","DOI":"10.1176\/appi.books.9780890425596"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1082","DOI":"10.1001\/jamapsychiatry.2016.1976","article-title":"Phenomenology of Schizophrenia and the Representativeness of Modern Diagnostic Criteria","volume":"73","author":"Kendler","year":"2016","journal-title":"JAMA Psychiatry"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"GeethaRamani, R., and Sivaselvi, K. (2014, January 18\u201320). Data mining technique for identification of diagnostic biomarker to predict Schizophrenia disorder. Proceedings of the 2014 IEEE International Conference on Computational Intelligence and Computing Research, Coimbatore, India.","DOI":"10.1109\/ICCIC.2014.7238525"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"45544","DOI":"10.1109\/ACCESS.2019.2908620","article-title":"A Machine Learning Approach for the Automatic Classification of Schizophrenic Discourse","volume":"7","author":"Zamora","year":"2019","journal-title":"IEEE Access"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.schres.2021.08.019","article-title":"The association of medical resource utilization with physical morbidity and premature mortality among patients with schizophrenia: An historical prospective population cohort study","volume":"237","author":"Lurie","year":"2021","journal-title":"Schizophr. Res."},{"key":"ref_16","first-page":"67","article-title":"Mental health and public health in Spain: Epidemiological surveillance and prevention","volume":"23","author":"Roca","year":"2016","journal-title":"Psiquiatr. Biol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1093\/epirev\/mxn001","article-title":"Schizophrenia: A Concise Overview of Incidence, Prevalence, and Mortality","volume":"30","author":"McGrath","year":"2008","journal-title":"Epidemiol. Rev."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1177\/1359786810385490","article-title":"Suicide and schizophrenia: A systematic review of rates and risk factors","volume":"24","author":"Hor","year":"2010","journal-title":"J. Psychopharmacol."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhang, L. (2019, January 23\u201327). EEG Signals Classification Using Machine Learning for The Identification and Diagnosis of Schizophrenia. Proceedings of the 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany.","DOI":"10.1109\/EMBC.2019.8857946"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"6405930","DOI":"10.1155\/2020\/6405930","article-title":"Detecting Abnormal Brain Regions in Schizophrenia Using Structural MRI via Machine Learning","volume":"2020","author":"Chen","year":"2020","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1007\/s40273-016-0444-6","article-title":"The Societal Cost of Schizophrenia: A Systematic Review","volume":"35","author":"Jin","year":"2017","journal-title":"PharmacoEconomics"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.eurpsy.2017.10.008","article-title":"Direct healthcare cost of schizophrenia\u2014European overview","volume":"48","author":"Kovacs","year":"2018","journal-title":"Eur. Psychiatry"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1007\/978-1-61779-458-2_37","article-title":"Data Mining in Psychiatric Research","volume":"829","author":"Tovar","year":"2012","journal-title":"Psychiatr. Disord."},{"key":"ref_24","first-page":"9917919","article-title":"A Comparative Analysis of Machine Learning Algorithms to Predict Alzheimer\u2019s Disease","volume":"2021","author":"Jamil","year":"2021","journal-title":"J. Healthc. Eng."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Bhagya Shree, S.R., and Sheshadri, H.S. (2014, January 18\u201320). An initial investigation in the diagnosis of Alzheimer\u2019s disease using various classification techniques. Proceedings of the 2014 IEEE International Conference on Computational Intelligence and Computing Research, Coimbatore, India.","DOI":"10.1109\/ICCIC.2014.7238300"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Sheshadri, H.S., Shree, S.R.B., and Krishna, M. (2015, January 24\u201327). Diagnosis of Alzheimer\u2019s Disease Employing Neuropsychological and Classification Techniques. Proceedings of the 2015 5th International Conference on IT Convergence and Security (ICITCS), Kuala Lumpur, Malaysia.","DOI":"10.1109\/ICITCS.2015.7292973"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.procs.2017.09.125","article-title":"Feature Selection Techniques for Prediction of Neuro-Degenerative Disorders: A Case-Study with Alzheimer\u2019s and Parkinson\u2019s Disease","volume":"115","author":"Tejeswinee","year":"2017","journal-title":"Procedia Comput. Sci."},{"key":"ref_28","first-page":"8","article-title":"A Prediction Model for Mild Cognitive Impairment Using Random Forests","volume":"6","author":"Byeon","year":"2015","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Cao, H., Meyer-Lindenberg, A., and Schwarz, E. (2018). Comparative Evaluation of Machine Learning Strategies for Analyzing Big Data in Psychiatry. Int. J. Mol. Sci., 19.","DOI":"10.3390\/ijms19113387"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.ijmedinf.2018.06.009","article-title":"A new computational intelligence approach to detect autistic features for autism screening","volume":"117","author":"Thabtah","year":"2018","journal-title":"Int. J. Med. Inform."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Bersimis, F.G., and Varlamis, I. (2019). Use of health-related indices and classification methods in medical data. Classification Techniques for Medical Image Analysis and Computer Aided Diagnosis, Elsevier Inc.","DOI":"10.1016\/B978-0-12-818004-4.00002-9"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"104068","DOI":"10.1016\/j.ijmedinf.2019.104068","article-title":"Comparison of supervised machine learning classification techniques in prediction of locoregional recurrences in early oral tongue cancer","volume":"136","author":"Alabi","year":"2020","journal-title":"Int. J. Med. Inform."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.schres.2020.01.008","article-title":"Using machine learning to predict mental healthcare consumption in non-affective psychosis","volume":"218","author":"Kwakernaak","year":"2020","journal-title":"Schizophr. Res."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"779684","DOI":"10.3389\/fpsyt.2021.779684","article-title":"The Importance of Suicide Risk Formulation in Schizophrenia","volume":"12","author":"Berardelli","year":"2021","journal-title":"Front. Psychiatry"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.genhosppsych.2017.03.001","article-title":"Classification of suicide attempters in schizophrenia using sociocultural and clinical features: A machine learning approach","volume":"47","author":"Hettige","year":"2017","journal-title":"Gen. Hosp. Psychiatry"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Almutairi, M.M., Alhamad, N., Alyami, A., Alshobbar, Z., Alfayez, H., Al-Akkas, N., Alhiyafi, J.A., and Olatunji, S.O. (2019, January 1\u20133). Preemptive Diagnosis of Schizophrenia Disease Using Computational Intelligence Techniques. Proceedings of the 2019 2nd International Conference on Computer Applications & Information Security (ICCAIS), Riyadh, Saudi Arabia.","DOI":"10.1109\/CAIS.2019.8769513"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1016\/j.schres.2016.05.007","article-title":"Machine-learning-based diagnosis of schizophrenia using combined sensor-level and source-level EEG features","volume":"176","author":"Shim","year":"2016","journal-title":"Schizophr. Res."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"e1818","DOI":"10.1002\/mpr.1818","article-title":"Diagnosing schizophrenia with network analysis and a machine learning method","volume":"29","author":"Jo","year":"2020","journal-title":"Int. J. Methods Psychiatr. Res."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Khan, S.I., Islam, A., Hossen, A., Zahangir, T.I., and Hoque, A.S.M.L. (2018, January 27\u201328). Supporting the Treatment of Mental Diseases using Data Mining. Proceedings of the 2018 International Conference on Innovations in Science, Engineering and Technology (ICISET), Chittagong, Bangladesh.","DOI":"10.1109\/ICISET.2018.8745591"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.pnpbp.2018.06.010","article-title":"Tractography-based classification in distinguishing patients with first-episode schizophrenia from healthy individuals","volume":"88","author":"Deng","year":"2019","journal-title":"Prog. Neuro Psychopharmacol. Biol. Psychiatry"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1016\/j.nicl.2018.02.007","article-title":"Diagnostic value of structural and diffusion imaging measures in schizophrenia","volume":"18","author":"Lee","year":"2018","journal-title":"NeuroImage Clin."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"135596","DOI":"10.1016\/j.neulet.2020.135596","article-title":"The machine learning algorithm for the diagnosis of schizophrenia on the basis of gene expression in peripheral blood","volume":"745","author":"Zhu","year":"2021","journal-title":"Neurosci. Lett."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"101698","DOI":"10.1016\/j.artmed.2019.07.006","article-title":"Automated detection of schizophrenia using nonlinear signal processing methods","volume":"100","author":"Jahmunah","year":"2019","journal-title":"Artif. Intell. Med."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1186\/s40810-016-0017-0","article-title":"Machine learning identification of EEG features predicting working memory performance in schizophrenia and healthy adults","volume":"2","author":"Johannesen","year":"2016","journal-title":"Neuropsychiatr. Electrophysiol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1109\/TBME.2016.2558824","article-title":"A Computer-Aided Diagnosis System with EEG Based on the P3b Wave During an Auditory Odd-Ball Task in Schizophrenia","volume":"64","author":"Arribas","year":"2017","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"103008","DOI":"10.1016\/j.ajp.2022.103008","article-title":"A bagging ensemble machine learning framework to predict overall cognitive function of schizo-phrenia patients with cognitive domains and tests","volume":"69","author":"Lin","year":"2022","journal-title":"Asian J. Psychiatr."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Bae, Y.J., Shim, M., and Lee, W.H. (2021). Schizophrenia Detection Using Machine Learning Approach from Social Media Content. Sensors, 21.","DOI":"10.3390\/s21175924"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"e289","DOI":"10.2196\/jmir.7956","article-title":"A Collaborative Approach to Identifying Social Media Markers of Schizophrenia by Employing Machine Learning and Clinical Appraisals","volume":"19","author":"Birnbaum","year":"2017","journal-title":"J. Med. Internet Res."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"112960","DOI":"10.1016\/j.psychres.2020.112960","article-title":"Prediction of physical violence in schizophrenia with machine learning algorithms","volume":"289","author":"Wang","year":"2020","journal-title":"Psychiatry Res."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1007\/s10916-018-1018-2","article-title":"Data Mining Algorithms and Techniques in Mental Health: A Systematic Review","volume":"42","author":"Alonso","year":"2018","journal-title":"J. Med. Syst."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"e15776","DOI":"10.2196\/15776","article-title":"Health Care Management Models for the Evolution of Hospitalization in Acute Inpatient Psychiatry Units: Comparative Quantitative Study","volume":"7","author":"Alonso","year":"2020","journal-title":"JMIR Ment. Health"},{"key":"ref_52","unstructured":"Commission on Professional and Hospital Activities (2021, May 06). The International Classification of Diseases, 9th Revision, Clinical Modi-Fication. Available online: https:\/\/www.msssi.gob.es\/estadEstudios\/estadisticas\/docs\/CIE9MC_2014_def_accesible.pdf."},{"key":"ref_53","unstructured":"CRAN.R-Project (2021, September 10). Dplyr Package. Available online: https:\/\/cran.r-project.org\/package=dplyr."},{"key":"ref_54","unstructured":"CRAN.R-Project (2021, September 10). Tidyr Package. Available online: https:\/\/cran.r-project.org\/package=tidyr."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Zamora Saiz, A., Quesada Gonz\u00e1lez, C., Hurtado Gil, L., and Mond\u00e9jar Ruiz, D. (2020). Data Analysis with R. An Introd to Data Anal R, Springer.","DOI":"10.1007\/978-3-030-48997-7_5"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"105524","DOI":"10.1016\/j.asoc.2019.105524","article-title":"Investigating the impact of data normalization on classification performance","volume":"97","author":"Singh","year":"2020","journal-title":"Appl. Soft Comput."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forest","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.aci.2018.12.004","article-title":"Predictive modelling and analytics for diabetes using a machine learning approach","volume":"18","author":"Kaur","year":"2022","journal-title":"Appl. Comput. Inform."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Abou-Warda, H., Belal, N.A., El-Sonbaty, Y., and Darwish, S. (2016, January 24\u201326). A Random Forest Model for Mental Disorders Diagnostic Systems. Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2016, Cairo, Egypt.","DOI":"10.1007\/978-3-319-48308-5_64"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Dvey-Aharon, Z., Fogelson, N., Peled, A., and Intrator, N. (2015). Schizophrenia Detection and Classification by Advanced Analysis of EEG Recordings Using a Single Electrode Approach. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0123033"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1038\/s41537-018-0070-8","article-title":"Towards artificial intelligence in mental health by improving schizophrenia prediction with multiple brain parcellation ensemble-learning","volume":"5","author":"Kalmady","year":"2019","journal-title":"NPJ Schizophr."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Xu, S., Yang, Z., Chakraborty, D., Tahir, Y., Maszczyk, T., Chua, V.Y.H., Dauwels, J., Thalmann, D., Thalmann, N.M., and Tan, B.-L. (2018, January 19\u201321). Automatic Verbal Analysis of Interviews with Schizophrenic Patients. Proceedings of the 2018 IEEE 23rd International Conference on Digital Signal Processing (DSP), Shanghai, China.","DOI":"10.1109\/ICDSP.2018.8631830"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.psychres.2019.03.048","article-title":"Relative importance of symptoms, cognition, and other multilevel variables for psychiatric disease classifications by machine learning","volume":"278","author":"Jiang","year":"2019","journal-title":"Psychiatry Res."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/7\/2517\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:43:13Z","timestamp":1760136193000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/7\/2517"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,25]]},"references-count":63,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2022,4]]}},"alternative-id":["s22072517"],"URL":"https:\/\/doi.org\/10.3390\/s22072517","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,25]]}}}