Hybrid Machine Learning Model for Body Fat Percentage Prediction Based on Support Vector Regression and Emotional Artificial Neural Networks
Obesity or excessive body fat causes multiple health problems and diseases. However, obesity treatment and control need an accurate determination of body fat percentage (BFP). The existing methods for BFP estimation require several procedures, which reduces their cost-effectivity and generalization....
Guardado en:
Autores principales: | Solaf A. Hussain, Nadire Cavus, Boran Sekeroglu |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
MDPI AG
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/91519aa093774c39a4bc4f8d7f3234a8 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Body Fat Percentage and Availability of Oral Food Intake: Prognostic Factors and Implications for Nutrition in Amyotrophic Lateral Sclerosis
por: Jin-Woo Park, et al.
Publicado: (2021) -
Optimal Body Fat Percentage Cut-Off Values in Predicting the Obesity-Related Cardiovascular Risk Factors: A Cross-Sectional Cohort Study
por: Macek P, et al.
Publicado: (2020) -
Day-Ahead Forecasting of the Percentage of Renewables Based on Time-Series Statistical Methods
por: Robert Basmadjian, et al.
Publicado: (2021) -
Assessment of Age-Induced Changes in Body Fat Percentage and BMI Aided by Bayesian Modelling: A Cross-Sectional Cohort Study in Middle-Aged and Older Adults
por: Macek P, et al.
Publicado: (2020) -
Performance of body mass index and percentage of body fat in predicting cardiometabolic risk factors in Thai adults
por: Vanavanan S, et al.
Publicado: (2018)