{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T08:24:15Z","timestamp":1791966255807,"version":"4.3.4"},"reference-count":62,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,3,3]],"date-time":"2025-03-03T00:00:00Z","timestamp":1740960000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Institute of Technology (University of Castilla-La Mancha, Spain)"},{"name":"Diputaci\u00f3n Provincial de Cuenca (Spain)"},{"name":"the Consorcio Hospital General Universitario De Valencia (Spain)"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Life"],"abstract":"<jats:p>Bipolar disorder (BD) is a complex psychiatric condition characterized by alternating episodes of mania and depression, posing significant challenges for accurate and timely diagnosis. This study explores the use of the Random Forest (RF) algorithm as a machine learning approach to classify patients with BD and healthy controls based on electroencephalogram (EEG) data. A total of 330 participants, including euthymic BD patients and healthy controls, were analyzed. EEG recordings were processed to extract key features, including power in frequency bands and complexity metrics such as the Hurst Exponent, which measures the persistence or randomness of a time series, and the Higuchi\u2019s Fractal Dimension, which is used to quantify the irregularity of brain signals. The RF model demonstrated robust performance, achieving an average accuracy of 93.41%, with recall and specificity exceeding 93%. These results highlight the algorithm\u2019s capacity to handle complex, noisy datasets while identifying key features relevant for classification. Importantly, the model provided interpretable insights into the physiological markers associated with BD, reinforcing the clinical value of EEG as a diagnostic tool. The findings suggest that RF is a reliable and accessible method for supporting the diagnosis of BD, complementing traditional clinical practices. Its ability to reduce diagnostic delays, improve classification accuracy, and optimize resource allocation make it a promising tool for integrating artificial intelligence into psychiatric care. This study represents a significant step toward precision psychiatry, leveraging technology to improve the understanding and management of complex mental health disorders.<\/jats:p>","DOI":"10.3390\/life15030394","type":"journal-article","created":{"date-parts":[[2025,3,3]],"date-time":"2025-03-03T09:04:49Z","timestamp":1740992689000},"page":"394","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Application of the Random Forest Algorithm for Accurate Bipolar Disorder Classification"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9794-7449","authenticated-orcid":false,"given":"Miguel","family":"Su\u00e1rez","sequence":"first","affiliation":[{"name":"Virgen de la Luz Hospital, 16002 Cuenca, Spain"},{"name":"Medical Analysis Expert Group, Institute of Technology, University of Castilla-La Mancha, 13001 Cuenca, Spain"},{"name":"Instituto de Investigaci\u00f3n Sanitaria de Castilla-La Mancha (IDISCAM), 45071 Toledo, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ana M.","family":"Torres","sequence":"additional","affiliation":[{"name":"Medical Analysis Expert Group, Institute of Technology, University of Castilla-La Mancha, 13001 Cuenca, Spain"},{"name":"Instituto de Investigaci\u00f3n Sanitaria de Castilla-La Mancha (IDISCAM), 45071 Toledo, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pilar","family":"Blasco-Segura","sequence":"additional","affiliation":[{"name":"Department of Pharmacy, General University Hospital, 46014 Valencia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jorge","family":"Mateo","sequence":"additional","affiliation":[{"name":"Medical Analysis Expert Group, Institute of Technology, University of Castilla-La Mancha, 13001 Cuenca, Spain"},{"name":"Instituto de Investigaci\u00f3n Sanitaria de Castilla-La Mancha (IDISCAM), 45071 Toledo, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1561","DOI":"10.1016\/S0140-6736(15)00241-X","article-title":"Bipolar disorder","volume":"387","author":"Grande","year":"2016","journal-title":"Lancet"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1001\/archpsyc.62.6.593","article-title":"Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the National Comorbidity Survey Replication","volume":"62","author":"Kessler","year":"2005","journal-title":"Arch. Gen. Psychiatry"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"804","DOI":"10.4088\/JCP.v61n1013","article-title":"Diagnosing bipolar disorder and the effect of antidepressants: A naturalistic study","volume":"61","author":"Ghaemi","year":"2000","journal-title":"J. Clin. Psychiatry"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1007\/s11920-024-01483-7","article-title":"Biomarkers of bipolar disorder in late life: An evidence-based systematic review","volume":"26","author":"Chancel","year":"2024","journal-title":"Curr. Psychiatry Rep."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1016\/j.jad.2024.07.011","article-title":"Synthesising 30 years of clinical experience and scientific insight on affective temperaments in psychiatric disorders: State of the art","volume":"362","author":"Favaretto","year":"2024","journal-title":"J. Affect. Disord."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1370","DOI":"10.1001\/jama.2023.18588","article-title":"Diagnosis and treatment of bipolar disorder: A review","volume":"330","author":"Nierenberg","year":"2023","journal-title":"JAMA"},{"key":"ref_7","first-page":"683","article-title":"American Psychiatric Association, Diagnostic and Statistical Manual of Mental Disorders: Dsm-5, Washington, DC, American Psychiatric Publishing, 2013. Ananth Mahesh, In defense of an evolutionary concept of health nature, norms, and human biology, Aldershot, England, Ashgate","volume":"39","author":"Oxford","year":"2014","journal-title":"Philosophy"},{"key":"ref_8","first-page":"179","article-title":"Bipolar disorders in the new DSM-5 and ICD-11 classifications","volume":"7","author":"Goikolea","year":"2014","journal-title":"Rev. Psiquiatr. Salud Ment. (Engl. Ed.)"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1841","DOI":"10.1016\/S0140-6736(20)31544-0","article-title":"Bipolar disorders","volume":"396","author":"McIntyre","year":"2020","journal-title":"Lancet"},{"key":"ref_10","first-page":"101135","article-title":"Bipolar disorders: An update on critical aspects","volume":"48","author":"Oliva","year":"2024","journal-title":"Lancet Reg. Health\u2013Eur."},{"key":"ref_11","first-page":"89","article-title":"Nosology, epidemiology and pathogenesis of bipolar disorder: Recent approaches","volume":"21","author":"Sierra","year":"2014","journal-title":"Psiquiatr. Biol\u00f3gica"},{"key":"ref_12","first-page":"237","article-title":"An overview of bipolar disorder diagnosis using machine learning approaches: Clinical opportunities and challenges","volume":"18","author":"Garay","year":"2023","journal-title":"Iran. J. Psychiatry"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Kabir, M.S., Khanom, J., Bhuiyan, M.A., Tumpa, Z.N., Rabby, S.F., and Bilgaiyan, S. (2023, January 23\u201325). The Early Detection of Dementia Disease Using Machine Learning Approach. Proceedings of the 2023 International Conference on Computer Communication and Informatics (ICCCI), Coimbatore, India.","DOI":"10.1109\/ICCCI56745.2023.10128411"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"e962","DOI":"10.1002\/hsr2.962","article-title":"Application of machine learning methods in predicting schizophrenia and bipolar disorders: A systematic review","volume":"6","author":"Montazeri","year":"2023","journal-title":"Health Sci. Rep."},{"key":"ref_15","first-page":"223","article-title":"Machine learning for precision psychiatry: Opportunities and challenges","volume":"3","author":"Bzdok","year":"2018","journal-title":"Biol. Psychiatry Cogn. Neurosci. Neuroimaging"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"570","DOI":"10.1055\/a-1525-7220","article-title":"Machine learning to predict outcomes in patients with acute pulmonary embolism who prematurely discontinued anticoagulant therapy","volume":"122","author":"Mora","year":"2022","journal-title":"Thromb. Haemost."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Soria, C., Arroyo, Y., Torres, A.M., Redondo, M.\u00c1., Basar, C., and Mateo, J. (2023). Method for classifying schizophrenia patients based on machine learning. J. Clin. Med., 12.","DOI":"10.3390\/jcm12134375"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Su\u00e1rez, M., Mart\u00ednez, R., Torres, A.M., Ram\u00f3n, A., Blasco, P., and Mateo, J. (2023). A Machine Learning-Based Method for Detecting Liver Fibrosis. Diagnostics, 13.","DOI":"10.3390\/diagnostics13182952"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Garrido, N.J., Gonz\u00e1lez-Mart\u00ednez, F., Losada, S., Plaza, A., Del Olmo, E., and Mateo, J. (2024). Innovation through Artificial Intelligence in Triage Systems for Resource Optimization in Future Pandemics. Biomimetics, 9.","DOI":"10.3390\/biomimetics9070440"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Su\u00e1rez, M., Gil-Rojas, S., Mart\u00ednez-Blanco, P., Torres, A.M., Ram\u00f3n, A., Blasco-Segura, P., Torralba, M., and Mateo, J. (2024). Machine Learning-Based Assessment of Survival and Risk Factors in Non-Alcoholic Fatty Liver Disease-Related Hepatocellular Carcinoma for Optimized Patient Management. Cancers, 16.","DOI":"10.3390\/cancers16061114"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1109\/MPRV.2022.3155728","article-title":"Long\u2013short ensemble network for bipolar manic-euthymic state recognition based on wrist-worn sensors","volume":"21","author":"Jakobsen","year":"2022","journal-title":"IEEE Pervasive Comput."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1038\/s41398-018-0334-0","article-title":"A signature-based machine learning model for distinguishing bipolar disorder and borderline personality disorder","volume":"8","author":"Goodwin","year":"2018","journal-title":"Transl. Psychiatry"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1177\/15500594221137234","article-title":"The deep learning method differentiates patients with bipolar disorder from controls with high accuracy using EEG data","volume":"55","author":"Metin","year":"2024","journal-title":"Clin. EEG Neurosci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"115378","DOI":"10.1016\/j.psychres.2023.115378","article-title":"Predicting outcome with Intranasal Esketamine treatment: A machine-learning, three-month study in Treatment-Resistant Depression (ESK-LEARNING)","volume":"327","author":"Pettorruso","year":"2023","journal-title":"Psychiatry Res."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Di Stefano, V., D\u2019Angelo, M., Monaco, F., Vignapiano, A., Martiadis, V., Barone, E., Fornaro, M., Steardo, L., Solmi, M., and Manchia, M. (2024). Decoding Schizophrenia: How AI-Enhanced fMRI Unlocks New Pathways for Precision Psychiatry. Brain Sci., 14.","DOI":"10.3390\/brainsci14121196"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Janeva, D., Krsteski, S., Tashkovska, M., Jovanovski, N., Kartalov, T., Taskovski, D., Ivanovski, Z., and Gerazov, B. (2023, January 27\u201329). A System for Differentiation of Schizophrenia and Bipolar Disorder based on rsfMRI. Proceedings of the 2023 30th International Conference on Systems, Signals and Image Processing (IWSSIP), Ohrid, North Macedonia.","DOI":"10.1109\/IWSSIP58668.2023.10180306"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Bader, M., Abdelwanis, M., Maalouf, M., and Jelinek, H.F. (2024). Detecting depression severity using weighted random forest and oxidative stress biomarkers. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-67251-y"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1485","DOI":"10.1002\/jad.12357","article-title":"Identifying the risk of depression in a large sample of adolescents: An artificial neural network based on random forest","volume":"96","author":"Zhou","year":"2024","journal-title":"J. Adolesc."},{"key":"ref_29","first-page":"41","article-title":"Applications of support vector machine (SVM) learning in cancer genomics","volume":"15","author":"Huang","year":"2018","journal-title":"Cancer Genom. Proteom."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Pisner, D.A., and Schnyer, D.M. (2020). Support vector machine. Machine Learning, Elsevier.","DOI":"10.1016\/B978-0-12-815739-8.00006-7"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"101829","DOI":"10.1016\/j.compmedimag.2020.101829","article-title":"Classification of malignant tumors in breast ultrasound using a pretrained deep residual network model and support vector machine","volume":"87","author":"Shia","year":"2021","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Javeed, A., Dallora, A.L., Berglund, J.S., Idrisoglu, A., Ali, L., Rauf, H.T., and Anderberg, P. (2023). Early prediction of dementia using feature extraction battery (feb) and optimized support vector machine (svm) for classification. Biomedicines, 11.","DOI":"10.3390\/biomedicines11020439"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1774","DOI":"10.1109\/TNNLS.2017.2673241","article-title":"Efficient kNN classification with different numbers of nearest neighbors","volume":"29","author":"Zhang","year":"2017","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Arian, R., Hariri, A., Mehridehnavi, A., Fassihi, A., and Ghasemi, F. (2020). Protein kinase inhibitors\u2019 classification using K-Nearest neighbor algorithm. Comput. Biol. Chem., 86.","DOI":"10.1016\/j.compbiolchem.2020.107269"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1176935120965542","DOI":"10.1177\/1176935120965542","article-title":"Robust distance measures for k NN classification of cancer data","volume":"19","author":"Ehsani","year":"2020","journal-title":"Cancer Inform."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Ghiasi, M.M., and Zendehboudi, S. (2021). Application of decision tree-based ensemble learning in the classification of breast cancer. Comput. Biol. Med., 128.","DOI":"10.1016\/j.compbiomed.2020.104089"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"102173","DOI":"10.1016\/j.datak.2023.102173","article-title":"Decision tree post-pruning without loss of accuracy using the SAT-PP algorithm with an empirical evaluation on clinical data","volume":"145","author":"Lazebnik","year":"2023","journal-title":"Data Knowl. Eng."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"102486","DOI":"10.1016\/j.artmed.2022.102486","article-title":"The voice of COVID-19: Breath and cough recording classification with temporal decision trees and random forests","volume":"137","author":"Manzella","year":"2023","journal-title":"Artif. Intell. Med."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2150178","DOI":"10.1142\/S0218126621501784","article-title":"Multi-feature analysis for automated brain stroke classification using weighted Gaussian na\u00efve Bayes classifier","volume":"30","author":"Jayachitra","year":"2021","journal-title":"J. Circuits Syst. Comput."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Gohari, K., Kazemnejad, A., Mohammadi, M., Eskandari, F., Saberi, S., Esmaieli, M., and Sheidaei, A. (2023). A Bayesian latent class extension of naive Bayesian classifier and its application to the classification of gastric cancer patients. BMC Med. Res. Methodol., 23.","DOI":"10.1186\/s12874-023-02013-4"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Queipo, M., Barbado, J., Torres, A.M., and Mateo, J. (2024). Approaching personalized medicine: The use of machine learning to determine predictors of mortality in a population with SARS-CoV-2 infection. Biomedicines, 12.","DOI":"10.3390\/biomedicines12020409"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Usategui, I., Arroyo, Y., Torres, A.M., Barbado, J., and Mateo, J. (2024). Systemic Lupus Erythematosus: How Machine Learning Can Help Distinguish between Infections and Flares. Bioengineering, 11.","DOI":"10.3390\/bioengineering11010090"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1016\/j.cmpb.2012.10.008","article-title":"Classifying depression patients and normal subjects using machine learning techniques and nonlinear features from EEG signal","volume":"109","author":"Hosseinifard","year":"2013","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_44","unstructured":"Lundberg, S.M., Erion, G.G., and Lee, S.-I. (2018). Consistent individualized feature attribution for tree ensembles. arXiv."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"121549","DOI":"10.1016\/j.eswa.2023.121549","article-title":"An improved random forest based on the classification accuracy and correlation measurement of decision trees","volume":"237","author":"Sun","year":"2024","journal-title":"Expert. Syst. Appl."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1007\/s004220050394","article-title":"Use of the fractal dimension for the analysis of electroencephalographic time series","volume":"77","author":"Accardo","year":"1997","journal-title":"Biol. Cybern."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"907","DOI":"10.1049\/iet-spr.2013.0113","article-title":"Monitoring the depth of anaesthesia using Hurst exponent and Bayesian methods","volume":"8","author":"Wen","year":"2014","journal-title":"IET Signal Process."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1201","DOI":"10.4088\/JCP.v64n1010","article-title":"Economic consequences of not recognizing bipolar disorder patients: A cross-sectional descriptive analysis","volume":"64","author":"Birnbaum","year":"2003","journal-title":"J. Clin. Psychiatry"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.jad.2006.06.018","article-title":"The impact of unrecognized bipolar disorders among patients treated for depression with antidepressants in the fee-for-services California Medicaid (Medi-Cal) program: A 6-year retrospective analysis","volume":"97","author":"McCombs","year":"2007","journal-title":"J. Affect. Disord."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1186\/2194-7511-1-12","article-title":"Bipolar disorders in DSM-5: Strengths, problems and perspectives","volume":"1","author":"Angst","year":"2013","journal-title":"Int. J. Bipolar Disord."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1192\/bjp.bp.114.152520","article-title":"Collaborative care for patients with bipolar disorder: Randomised controlled trial","volume":"206","author":"Goossens","year":"2015","journal-title":"Br. J. Psychiatry"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1186\/2194-7511-1-14","article-title":"Diagnosing bipolar disorders in DSM-5","volume":"1","author":"Severus","year":"2013","journal-title":"Int. J. Bipolar Disord."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.jad.2015.03.005","article-title":"Collaborative care for patients with bipolar disorder: Effects on functioning and quality of life","volume":"179","author":"Hoogendoorn","year":"2015","journal-title":"J. Affect. Disord."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1007\/s11265-016-1159-9","article-title":"Discriminating bipolar disorder from major depression using whole-brain functional connectivity: A feature selection analysis with SVM-FoBa algorithm","volume":"90","author":"Jie","year":"2018","journal-title":"J. Signal Process. Syst."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1109\/TAFFC.2020.3047582","article-title":"A multi-modal stacked ensemble model for bipolar disorder classification","volume":"14","author":"AbaeiKoupaei","year":"2020","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"16756","DOI":"10.1109\/ACCESS.2022.3146711","article-title":"Exploration of EEG-based depression biomarkers identification techniques and their applications: A systematic review","volume":"10","author":"Dev","year":"2022","journal-title":"IEEE Access"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Yamunarani, T., Ponniran, A.B., Zaki, W.S.B.W., Ali, R.A.B.M., and Sivaranjani, S. (2023, January 19\u201320). EEG\u2013Based Bipolar Disorder Deduction Using Machine Learning. Proceedings of the 2023 First International Conference on Advances in Electrical, Electronics and Computational Intelligence (ICAEECI), Tiruchengode, India.","DOI":"10.1109\/ICAEECI58247.2023.10370962"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.cmpb.2018.04.012","article-title":"Automated EEG-based screening of depression using deep convolutional neural network","volume":"161","author":"Acharya","year":"2018","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2021.3053999","article-title":"DeprNet: A deep convolution neural network framework for detecting depression using EEG","volume":"70","author":"Seal","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Yasin, S., Hussain, S.A., Aslan, S., Raza, I., Muzammel, M., and Othmani, A. (2021). EEG based Major Depressive disorder and Bipolar disorder detection using Neural Networks: A review. Comput. Methods Programs Biomed., 202.","DOI":"10.1016\/j.cmpb.2021.106007"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Nazari, M.-J., Shalbafan, M., Eissazade, N., Khalilian, E., Vahabi, Z., Masjedi, N., Ghidary, S.S., Saadat, M., and Sadegh-Zadeh, S.-A. (2024). A machine learning approach for differentiating bipolar disorder type II and borderline personality disorder using electroencephalography and cognitive abnormalities. PLoS ONE, 19.","DOI":"10.1371\/journal.pone.0303699"}],"container-title":["Life"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-1729\/15\/3\/394\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:46:06Z","timestamp":1760028366000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-1729\/15\/3\/394"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,3]]},"references-count":62,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025,3]]}},"alternative-id":["life15030394"],"URL":"https:\/\/doi.org\/10.3390\/life15030394","relation":{},"ISSN":["2075-1729"],"issn-type":[{"value":"2075-1729","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,3]]}}}