Predicting Executive Function Impairments in Young Adults Using Machine Learning and Lifestyle Data.
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| Title: | Predicting Executive Function Impairments in Young Adults Using Machine Learning and Lifestyle Data. |
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| Alternate Title: | Predicción del deterioro de las funciones ejecutivas en adultos jóvenes utilizando aprendizaje automático y datos de estilo de vida. |
| Authors: | Jarillo Silva, Alejandro1 ajarillo@unsis.edu.mx, Muñoz Ortiz, Elizabeth2, Montes de Oca Juárez, Olaf1, Cruz Torentino, José Alberto1 |
| Source: | Revista Mexicana de Ingeniería Biomédica. Jan-Apr2026, Vol. 47 Issue 1, p1-15. 15p. |
| Subjects: | EXECUTIVE function, MACHINE learning, SOCIODEMOGRAPHIC factors, PREDICTION models, NUTRITION, COGNITIVE testing, YOUNG adults |
| Abstract (English): | The development of executive function (EF) impairments in young individuals, such as difficulties with attention, memory, and problem-solving, is influenced by biological, social, and lifestyle factors. However, research on predicting these impairments remains limited due to a lack of reliable tools. This study analyzed 90 university students using EF tests, lifestyle, and sociodemographic questionnaires. Five machine learning models were evaluated: Decision Trees (DT), kNearest Neighbors (KNN), Support Vector Machines (SVM), Logistic Regression (LR), and Random Forest (RF), with crossvalidation applied for model assessment. The results indicated a 62% incidence of EF impairments. Maternal education and nutrition were identified as key influencing factors. Among the models, DT performed best, achieving a recall of 61.9%, an F1-score of 62.1%, and an AUC of 66.54%, while RF had the lowest performance. Limitations include the cross-sectional nature of the data, which restricts causal inference, and the reliance on self-reported responses from participants, which may reduce data reliability. Despite these limitations, this study demonstrates the feasibility of using machine learning to predict EF impairments based on easily collected sociodemographic and lifestyle data. Sociodemographic and lifestyle variables are valuable predictors of EF impairments in young individuals. Machine learning tools offer a practical approach to assessing population-level EF health using accessible data. [ABSTRACT FROM AUTHOR] |
| Abstract (Spanish): | El desarrollo de deterioros en las funciones ejecutivas (FE) en jóvenes, como dificultades en la atención, la memoria y la resolución de problemas, está influenciado por factores biológicos, sociales y de estilo de vida. Sin embargo, la investigación sobre la predicción de estos deterioros sigue siendo limitada debido a la falta de herramientas confiables. Este estudio analizó a 90 estudiantes universitarios mediante pruebas de FE y cuestionarios sobre estilo de vida y factores sociodemográficos. Se evaluaron cinco modelos de aprendizaje automático: Árboles de Decisión (DT), k-Nearest Neighbors (KNN), Máquinas de Soporte Vectorial (SVM), Regresión Logística (LR) y Bosques Aleatorios (RF), aplicando validación cruzada para la evaluación de los modelos. Los resultados indicaron una incidencia del 62% en deterioros de las FE. Se identificaron la educación materna y la nutrición como factores clave influyentes. Entre los modelos, DT obtuvo el mejor desempeño, con una sensibilidad del 61.9%, un F1-score de 62.1% y un AUC de 66.54%, mientras que RF tuvo el peor rendimiento. Las limitaciones incluyen la naturaleza transversal de los datos, lo que restringe la inferencia causal, y la dependencia de respuestas autoinformadas por los participantes, lo que podría afectar la fiabilidad de los datos. A pesar de esto, el estudio demuestra la viabilidad del uso de aprendizaje automático para predecir deterioros en las FE con datos sociodemográficos y de estilo de vida fácilmente recopilables. Las variables sociodemográficas y de estilo de vida son valiosos predictores de deterioros en las FE en jóvenes. Las herramientas de aprendizaje automático ofrecen un enfoque práctico para evaluar la salud de las FE a nivel poblacional utilizando datos accesibles. [ABSTRACT FROM AUTHOR] |
| 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. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | MedicLatina |
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| Header | DbId: lth DbLabel: MedicLatina An: 194405331 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Predicting Executive Function Impairments in Young Adults Using Machine Learning and Lifestyle Data. – Name: TitleAlt Label: Alternate Title Group: TiAlt Data: Predicción del deterioro de las funciones ejecutivas en adultos jóvenes utilizando aprendizaje automático y datos de estilo de vida. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Jarillo+Silva%2C+Alejandro%22">Jarillo Silva, Alejandro</searchLink><relatesTo>1</relatesTo><i> ajarillo@unsis.edu.mx</i><br /><searchLink fieldCode="AR" term="%22Muñoz+Ortiz%2C+Elizabeth%22">Muñoz Ortiz, Elizabeth</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Montes+de+Oca+Juárez%2C+Olaf%22">Montes de Oca Juárez, Olaf</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Cruz+Torentino%2C+José+Alberto%22">Cruz Torentino, José Alberto</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Revista+Mexicana+de+Ingeniería+Biomédica%22">Revista Mexicana de Ingeniería Biomédica</searchLink>. Jan-Apr2026, Vol. 47 Issue 1, p1-15. 15p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22EXECUTIVE+function%22">EXECUTIVE function</searchLink><br /><searchLink fieldCode="DE" term="%22MACHINE+learning%22">MACHINE learning</searchLink><br /><searchLink fieldCode="DE" term="%22SOCIODEMOGRAPHIC+factors%22">SOCIODEMOGRAPHIC factors</searchLink><br /><searchLink fieldCode="DE" term="%22PREDICTION+models%22">PREDICTION models</searchLink><br /><searchLink fieldCode="DE" term="%22NUTRITION%22">NUTRITION</searchLink><br /><searchLink fieldCode="DE" term="%22COGNITIVE+testing%22">COGNITIVE testing</searchLink><br /><searchLink fieldCode="DE" term="%22YOUNG+adults%22">YOUNG adults</searchLink> – Name: Abstract Label: Abstract (English) Group: Ab Data: The development of executive function (EF) impairments in young individuals, such as difficulties with attention, memory, and problem-solving, is influenced by biological, social, and lifestyle factors. However, research on predicting these impairments remains limited due to a lack of reliable tools. This study analyzed 90 university students using EF tests, lifestyle, and sociodemographic questionnaires. Five machine learning models were evaluated: Decision Trees (DT), kNearest Neighbors (KNN), Support Vector Machines (SVM), Logistic Regression (LR), and Random Forest (RF), with crossvalidation applied for model assessment. The results indicated a 62% incidence of EF impairments. Maternal education and nutrition were identified as key influencing factors. Among the models, DT performed best, achieving a recall of 61.9%, an F1-score of 62.1%, and an AUC of 66.54%, while RF had the lowest performance. Limitations include the cross-sectional nature of the data, which restricts causal inference, and the reliance on self-reported responses from participants, which may reduce data reliability. Despite these limitations, this study demonstrates the feasibility of using machine learning to predict EF impairments based on easily collected sociodemographic and lifestyle data. Sociodemographic and lifestyle variables are valuable predictors of EF impairments in young individuals. Machine learning tools offer a practical approach to assessing population-level EF health using accessible data. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Abstract (Spanish) Group: Ab Data: El desarrollo de deterioros en las funciones ejecutivas (FE) en jóvenes, como dificultades en la atención, la memoria y la resolución de problemas, está influenciado por factores biológicos, sociales y de estilo de vida. Sin embargo, la investigación sobre la predicción de estos deterioros sigue siendo limitada debido a la falta de herramientas confiables. Este estudio analizó a 90 estudiantes universitarios mediante pruebas de FE y cuestionarios sobre estilo de vida y factores sociodemográficos. Se evaluaron cinco modelos de aprendizaje automático: Árboles de Decisión (DT), k-Nearest Neighbors (KNN), Máquinas de Soporte Vectorial (SVM), Regresión Logística (LR) y Bosques Aleatorios (RF), aplicando validación cruzada para la evaluación de los modelos. Los resultados indicaron una incidencia del 62% en deterioros de las FE. Se identificaron la educación materna y la nutrición como factores clave influyentes. Entre los modelos, DT obtuvo el mejor desempeño, con una sensibilidad del 61.9%, un F1-score de 62.1% y un AUC de 66.54%, mientras que RF tuvo el peor rendimiento. Las limitaciones incluyen la naturaleza transversal de los datos, lo que restringe la inferencia causal, y la dependencia de respuestas autoinformadas por los participantes, lo que podría afectar la fiabilidad de los datos. A pesar de esto, el estudio demuestra la viabilidad del uso de aprendizaje automático para predecir deterioros en las FE con datos sociodemográficos y de estilo de vida fácilmente recopilables. Las variables sociodemográficas y de estilo de vida son valiosos predictores de deterioros en las FE en jóvenes. Las herramientas de aprendizaje automático ofrecen un enfoque práctico para evaluar la salud de las FE a nivel poblacional utilizando datos accesibles. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>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. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.17488/RMIB.47.1.1550 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 1 Subjects: – SubjectFull: EXECUTIVE function Type: general – SubjectFull: MACHINE learning Type: general – SubjectFull: SOCIODEMOGRAPHIC factors Type: general – SubjectFull: PREDICTION models Type: general – SubjectFull: NUTRITION Type: general – SubjectFull: COGNITIVE testing Type: general – SubjectFull: YOUNG adults Type: general Titles: – TitleFull: Predicting Executive Function Impairments in Young Adults Using Machine Learning and Lifestyle Data. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Jarillo Silva, Alejandro – PersonEntity: Name: NameFull: Muñoz Ortiz, Elizabeth – PersonEntity: Name: NameFull: Montes de Oca Juárez, Olaf – PersonEntity: Name: NameFull: Cruz Torentino, José Alberto IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan-Apr2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 01889532 Numbering: – Type: volume Value: 47 – Type: issue Value: 1 Titles: – TitleFull: Revista Mexicana de Ingeniería Biomédica Type: main |
| ResultId | 1 |
