Random Forest modeling of bipolar affective disorder in Ecuador
DOI:
https://doi.org/10.56294/saludcyt20251970Keywords:
Algorithms, Machine Learning, Decision Tree Gradient Boosting, Random ForestAbstract
Bipolar affective disorder is a mental disorder characterized by depressive and manic or hypomanic episodes. The complexity of the diagnosis of bipolar affective disorder due to the overlapping of its symptoms with other mood disorders led researchers and doctors to search for new and advanced techniques for the precise detection of bipolar affection disorder. One of these methods is the use of advanced machine learning algorithms under a statistical methodology for building logistical regression models, Random Forest. Support vector machines, Decision Tree, K-Nearest Neighbors, and Gradient Boosting, with 146 data collected from the psychiatric services affiliated with the mental health system of Ecuador. At the inferential level, the results suggest that the implementation of automatic algorithms based on the different methodologies for building models enables the successful prediction or classification of individuals with bipolar affective disorders in Ecuador compared to controlled patients who do not profile under this pathological picture. It is the best Random Forest statistical model (89.35 %) that dictates the best performance metrics compared to the Gradient Boosting model. The evolution of the overall prevalence of bipolar affective disorders in Ecuador over the past 22 years has increased by a small differential. However, from 2020 to 2022, there has been a considerable increase in the percentage prevalence of cases of bipolar affective disorders in Ecuador.
References
1. OMS. Psychosis and bipolar disorders [Internet]. Who.int. [citado el 9 de enero de 2025]. Disponible en: https://www.who.int/teams/mental-health-and-substance-use/treatment-care/mental-health-gap-action-programme/evidence-centre/psychosis-
2. Weye N, Momen NC, Christensen MK, Iburg KM, Dalsgaard S, Laursen TM, et al. Association of specific mental disorders with premature mortality in the danish population using alternative measurement methods. JAMA Netw Open. 2020;3(6):e206646. http://dx.doi.org/10.1001/jamanetworkopen.2020.6646
3. Biazus TB, Beraldi GH, Tokeshi L, Rotenberg L de S, Dragioti E, Carvalho AF, et al. All-cause and cause-specific mortality among people with bipolar disorder: a large-scale systematic review and meta-analysis. Mol Psychiatry. 2023;28(6):2508–24. http://dx.doi.org/10.1038/s41380-023-02109-9
4. McIntyre RS, Alda M, Baldessarini RJ, Bauer M, Berk M, Correll CU, et al. The clinical characterization of the adult patient with bipolar disorder aimed at personalization of management. World Psychiatry. 2022;21(3):364–87. http://dx.doi.org/10.1002/wps.20997
5. Chakrabarti S, Singh N. Psychotic symptoms in bipolar disorder and their impact on the illness: A systematic review. World J Psychiatry. 2022;12(9):1204–32. http://dx.doi.org/10.5498/wjp.v12.i9.1204
6. Holm M, Taipale H, Tanskanen A, Tiihonen J, Mitterdorfer-Rutz E. Employment among people with schizophrenia or bipolar disorder: A population‐based study using nationwide registers. Acta Psychiatr Scand. 2021;143(1):61–71. http://dx.doi.org/10.1111/acps.13254
7. Joas E, Bäckman K, Karanti A, Sparding T, Colom F, Pålsson E, et al. Psychoeducation for bipolar disorder and risk of recurrence and hospitalization – a within-individual analysis using registry data. Psychol Med. 2020;50(6):1043–9. http://dx.doi.org/10.1017/s0033291719001053
8. Walsh CG, Ripperger MA, Hu Y, Sheu Y-H, Lee H, Wilimitis D, et al. Development and multi-site external validation of a generalizable risk prediction model for bipolar disorder. Transl Psychiatry. 2024;14(1). http://dx.doi.org/10.1038/s41398-023-02720-y
9. Rantala MJ, Luoto S, Borráz-León JI, Krams I. Bipolar disorder: An evolutionary psychoneuroimmunological approach. Neurosci Biobehav Rev. 2021;122:28–37. http://dx.doi.org/10.1016/j.neubiorev.2020.12.031
10. Cheon E-J, Bearden CE, Sun D, Ching CRK, Andreassen OA, Schmaal L, et al. Cross disorder comparisons of brain structure in schizophrenia, bipolar disorder, major depressive disorder, and 22q11.2 deletion syndrome: A review of ENIGMA findings. Psychiatry Clin Neurosci [Internet]. 2022;76(5):140–61. Disponible en: http://dx.doi.org/10.1111/pcn.13337
11. Malhi GS, Bell E, Bassett D, Boyce P, Bryant R, Hazell P, et al. The 2020 Royal Australian and New Zealand College of Psychiatrists clinical practice guidelines for mood disorders. Aust N Z J Psychiatry. 2021;55(1):7–117. http://dx.doi.org/10.1177/0004867420979353
12. Di Salvo G, Porceddu G, Albert U, Maina G, Rosso G. Correlates of long duration of untreated illness (DUI) in patients with bipolar disorder: results of an observational study. Ann Gen Psychiatry. 2023;22(1). http://dx.doi.org/10.1186/s12991-023-00442-5
13. IHME Bipolar disorder prevalence [Internet]. Our World in Data. [citado el 9 de enero de 2024]. Disponible en: https://ourworldindata.org/grapher/bipolar-disorder-prevalence?tab=chart&time=earliest..latest&country=~ECU
14. INEC Ecuador - Estadísticas Hospitalarias Camas y Egresos 2022 [Internet]. Gob.ec. [citado el 16 de mayo de 2024]. Disponible en: https://anda.inec.gob.ec/anda/index.php/catalog/976/datafile/F47/V1715
15. Carruthers SP, Rossell SL, Murray G, Karantonis J, Furlong LS, Van Rheenen TE. Mindfulness, mood symptom tendencies and quality of life in bipolar disorder: An examination of the mediating influence of emotion regulation difficulties. J Affect Disord. 2022;298:166–72. http://dx.doi.org/10.1016/j.jad.2021.10.107
16. Lalli M, Brouillette K, Kapczinski F, de Azevedo Cardoso T. Substance use as a risk factor for bipolar disorder: A systematic review. J Psychiatr Res. 2021;144:285–95. http://dx.doi.org/10.1016/j.jpsychires.2021.10.012
17. Rodriguez V, Alameda L, Trotta G, Spinazzola E, Marino P, Matheson SL, et al. Environmental risk factors in Bipolar disorder and Psychotic depression: A systematic review and meta-analysis of prospective studies. Schizophr Bull. 2021;47(4):959–74. http://dx.doi.org/10.1093/schbul/sbaa197
18. He H, Hu C, Ren Z, Bai L, Gao F, Lyu J. Trends in the incidence and DALYs of bipolar disorder at global, regional, and national levels: Results from the global burden of Disease Study 2017. J Psychiatr Res. 2020;125:96–105. http://dx.doi.org/10.1016/j.jpsychires.2020.03.015
19. Palacios-Ariza MA, Morales-Mendoza E, Murcia J, Arias-Duarte R, Lara-Castellanos G, Cely-Jiménez A, et al. Prediction of patient admission and readmission in adults from a Colombian cohort with bipolar disorder using artificial intelligence. Front Psychiatry. 2023;14. http://dx.doi.org/10.3389/fpsyt.2023.1266548
20. Montazeri M, Montazeri M, Bahaadinbeigy K, Montazeri M, Afraz A. Application of machine learning methods in predicting schizophrenia and bipolar disorders: A systematic review. Health Sci Rep. 2023;6(1). http://dx.doi.org/10.1002/hsr2.962
21. Wanderley Espinola C, Gomes JC, Mônica Silva Pereira J, dos Santos WP. Detection of major depressive disorder, bipolar disorder, schizophrenia and generalized anxiety disorder using vocal acoustic analysis and machine learning: an exploratory study. Res Biomed Eng. 2022;38(3):813–29. http://dx.doi.org/10.1007/s42600-022-00222-2
22. Cearns M, Amare AT, Schubert KO, Thalamuthu A, Frank J, Streit F, et al. Using polygenic scores and clinical data for bipolar disorder patient stratification and lithium response prediction: machine learning approach. Br J Psychiatry. 2022;220(4):219–28. http://dx.doi.org/10.1192/bjp.2022.28
23. Llamocca P, López V, Santos M, Čukić M. Personalized characterization of emotional states in patients with bipolar disorder. Mathematics. 2021;9(11):1174. http://dx.doi.org/10.3390/math9111174
24. Gordovez FJA, McMahon FJ. The genetics of bipolar disorder. Mol Psychiatry. 2020;25(3):544–59. http://dx.doi.org/10.1038/s41380-019-0634-7
25. Mikolas P, Bröckel K, Vogelbacher C, Müller DK, Marxen M, Berndt C, et al. Individuals at increased risk for development of bipolar disorder display structural alterations similar to people with manifest disease. Transl Psychiatry. 2021;11(1). http://dx.doi.org/10.1038/s41398-021-01598-y
26. Comsa M, Anderson KN, Sharma A, Yadav VC, Watson S. The relationship between sleep and depression and bipolar disorder in children and young people. BJPsych Open. 2022;8(1). http://dx.doi.org/10.1192/bjo.2021.1076
27. Orsolini L, Fiorani M, Volpe U. Digital phenotyping in bipolar disorder: Which integration with clinical endophenotypes and biomarkers? Int J Mol Sci. 2020;21(20):7684. http://dx.doi.org/10.3390/ijms21207684
28. Sonkurt HO, Altınöz AE, Çimen E, Köşger F, Öztürk G. The role of cognitive functions in the diagnosis of bipolar disorder: A machine learning model. Int J Med Inform. 2021;145(104311):104311. http://dx.doi.org/10.1016/j.ijmedinf.2020.104311.
Published
Issue
Section
License
Copyright (c) 2025 Cristhian Ismael Gómez Gaona, Andrea del Rocío Mejía Rubio, José Rubén León Pérez, Jesús Rodríguez, Zilma Diago Alfes, Laura Esther Muñoz Escobar, Cristian Inca, Jimmy Yaguana Torres, Marco Velasco Arellano (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.
