Comparison of Machine Learning Models for Identification of Depressive Patients through Motor Activity.
The present study aims to evaluate various classification algorithms for data pertaining to subjects diagnosed with depression and non-depressive subjects. To this end, the data obtained from the "depresjon" dataset proposed by Garcia-Ceja, E., et al were analyzed. This dataset comprises motor activ...
| Published in: | Revista Mexicana de Ingeniería Biomédica Vol. 46; no. 1; pp. 1 - 18 |
|---|---|
| Main Authors: | , , , , |
| Format: | Article |
| Published: |
Sociedad Mexicana de Ingenieria Biomedica, A.C.
Jan-Apr2025
|
| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=lth&AN=185172915&site=ehost-live header: @attributes: shortDbName: lth uiTerm: 185172915 longDbName: MedicLatina uiTag: AN controlInfo: bkinfo: jinfo: jid: 01889532 7L3 jtl: Revista Mexicana de Ingeniería Biomédica issn: 01889532 maglogo: N pubinfo: dt: Jan-Apr2025 vid: 46 iid: 1 pid: 20850 pub: Sociedad Mexicana de Ingenieria Biomedica, A.C. artinfo: ui: 185172915 10.17488/RMIB.46.1.1487 ppf: 1 ppct: 17 formats: fmt: @attributes: type: P size: 1.6MB tig: atl: Comparison of Machine Learning Models for Identification of Depressive Patients through Motor Activity. aug: au: Neftalí Rivera-Rojas, Gerardo Eric Galván-Tejada, Carlos Isaac Galván-Tejeda, Jorge Celaya-Padilla, José M. Acosta-Cruz, Erika affil: Universidad Autónoma de Zacatecas, Zacatecas - México Universidad Autónoma de Coahuila, Coahuila - México su: Data mining Classification algorithms Motor learning Data analysis sug: subj: Data mining Classification algorithms Motor learning Data analysis keyword: data analysis data mining depression machine learning and motor activity actividad motora análisis de datos aprendizaje automático depresión minería de datos ab: The present study aims to evaluate various classification algorithms for data pertaining to subjects diagnosed with depression and non-depressive subjects. To this end, the data obtained from the "depresjon" dataset proposed by Garcia-Ceja, E., et al were analyzed. This dataset comprises motor activity recorded by the Actiwatch device (Cambridge Neurotechnology Ltd, England, model AW4). Predictions were made using various machine learning models, including synthetic data. Subsequently, metrics such as specificity, sensitivity, and precision were compared. The results highlight the best features of the data and the best machine learning model (using an ensemble model) for classifying potential depressive episodes in activity during the afternoon and night, with a precision of 96.6 %, sensitivity of 100 %, and specificity of 93.33 %. El presente estudio tiene como objetivo evaluar diversos algoritmos de clasificación de datos pertenecientes a sujetos diagnosticados con depresión y sujetos no depresivos. Para ello, se analizaron los datos obtenidos del dataset "depresjon" propuesto por Garcia-Ceja, E., et al, el cual se compone de la actividad motora captada por el dispositivo Actiwatch (Cambridge Neurotechnology Ltd, England, model AW4). Mediante distintos modelos de aprendizaje automático se realizaron predicciones incluyendo datos sintéticos. Posteriormente, se compararon métricas como especificidad, sensibilidad y precisión. Los resultados muestran las mejores características de los datos, así como el mejor modelo de aprendizaje automático (mediante modelo de ensamble) para realizar la clasificación de posibles episodios depresivos en la actividad durante la tarde y la noche, con una precisión del 96.6 %, una sensibilidad del 100 % y una especificidad del 93.33 %. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Copyright of Revista Mexicana de Ingeniería Biomédica is the property of Sociedad Mexicana de Ingenieria Biomedica, A.C. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. item: Revista Mexicana de Ingeniería Biomédica holder: Sociedad Mexicana de Ingenieria Biomedica, A.C. dt: @attributes: year: 2025 holdings: @attributes: islocal: N |
|---|