CLASIFICACIÓN DE NIVELES DE OBESIDAD MEDIANTE MODELOS DE MACHINE LEARNING: COMPARACIÓN ENTRE RANDOM FOREST, SVM Y REGRESIÓN LOGÍSTICA DESDE UNA PERSPECTIVA DE INTELIGENCIA ARTIFICIAL CLÍNICA
DOI:
https://doi.org/10.56238/arev7n12-107Palabras clave:
Machine Learning, Inteligencia Artificial, Obesidad, Random Forest, Salud DigitalResumen
El aumento global de la obesidad ha intensificado la necesidad de herramientas analíticas capaces de mejorar el diagnóstico y la estratificación del riesgo. Este estudio evalúa tres modelos de Aprendizaje Automático (Random Forest, Support Vector Machine y Regresión Logística Multinomial) para clasificar niveles de obesidad en adultos. El pipeline incluye preprocesamiento, imputación, codificación categórica, normalización, validación cruzada y evaluación multicriterio. Se incorporaron técnicas modernas de interpretabilidad basadas en Permutation Importance, permitiendo cuantificar el impacto de cada variable en la métrica F1-macro desde una perspectiva de Inteligencia Artificial clínica. También se implementó una línea base clínica basada únicamente en el Índice de Masa Corporal. Los resultados muestran que Random Forest presenta el mejor rendimiento, superando la línea base y los demás modelos. Los hallazgos refuerzan el potencial del Aprendizaje Automático como herramienta de apoyo en salud digital.
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