Enhancing Obesity Detection Through SMOTE -based Classification Models: A comparative Study
DOI:
https://doi.org/10.18041/2665-427X/ijeph.1.11532Palabras clave:
Obesidad, Técnica de Sobremuestreo de Minorías Sintéticas, smote, ]datos desbalanceados, toma de decisiones en saludResumen
Objetivo: Utilizar Técnica de Sobre muestreo de Minorías Sintéticas (SMOTE) para mejorar el equilibrio de clases y comparar el rendimiento de distintos métodos de clasificación antes y después de aplicar SMOTE
Métodos: Los métodos de clasificación fueron Regresión Logística, Naive Bayes, KNN (k=5) y Aprendizaje Profundo. Cada modelo fue entrenado y probado en el conjunto de datos, antes y después de aplicar SMOTE. Se utilizaron las métricas de evaluación: Precisión, Sensibilidad, Especificidad, Precisión equilibrada, Puntuación F1.
Resultados: Modelos como la regresión logística y Naive Bayes tuvieron problemas con sensibilidad y especificidad bajas, KNN (k=5) mostró una especificidad deficiente. Con SMOTE, se observaron mejoras significativas en todos los modelos. La regresión logística, a pesar de una disminución de la precisión (-8.8), la sensibilidad y la especificidad aumentaron sustancialmente (+56.7%), y mejoró la precisión equilibrada (+16.6%). Naive Bayes experimentó un modesto aumento de la precisión (+2.3%), mejoró la sensibilidad y la especificidad (+47.9%). El clasificador KNN mostró una mejora transformadora con aumento de la sensibilidad, la especificidad (+96.0%) y precisión equilibrada (+28.3%). El aprendizaje profundo mostró aumento significativo de sensibilidad (+69.8%), exactitud equilibrada (+29.4%) y una mejora notable de la precisión y la puntuación F1 a pesar de un ligero descenso de la especificidad (-10.9%).
Conclusiones: SMOTE contribuye a realizar predicciones más exactas y fiables. Aunque puede haber ligeras desventajas, las mejoras generales en las métricas usadas confirman la utilidad de SMOTE para mejorar el rendimiento de los modelos en conjuntos de datos desequilibrados.
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