A review of the application of deep learning in obesity: From early prediction aid to advanced management assistance

Yi, Xinghao; He, Yangzhige; Gao, Shan; Li, Ming

doi:10.1016/j.dsx.2024.103000

Cited by 3 publications

(1 citation statement)

References 73 publications

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“…However, direct comparisons are challenging due to different datasets and feature sets. Yi et al [40] employed deep learning with convolutional neural networks (CNNs) for obesity prediction based on body images, achieving an accuracy of 91.7%. While innovative, their approach relies on visual data rather than the demographic, lifestyle, and health-related features used in our study Muse et al [18] used a combination of feature selection techniques and ML algorithms, including support vector machines and artificial neural networks, for obesity prediction.…”

Section: Discussionmentioning

confidence: 99%

Predicting Obesity Levels with High Accuracy: Insights from a CatBoost Machine Learning Model

Maulana,

Afidh,

Maulydia

et al. 2024

Infolitika J. Data Sci.

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This study aims to develop a machine learning model using the CatBoost algorithm to predict obesity based on demographic, lifestyle, and health-related features and compare its performance with other machine learning algorithms. The dataset used in this study, containing information on 2,111 individuals from Mexico, Peru, and Colombia, was used to train and evaluate the CatBoost model. The dataset included gender, age, height, weight, eating habits, physical activity levels, and family history of obesity. The model's performance was assessed using accuracy, precision, recall, and F1-score and compared to logistic regression, K-nearest neighbors (KNN), random forest, and naive Bayes algorithms. Feature importance analysis was conducted to identify the most influential factors in predicting obesity levels. The results indicate that the CatBoost model achieved the highest accuracy at 95.98%, surpassing other models. Furthermore, the CatBoost model demonstrated superior precision (96.08%), recall (95.98%), and F1-score (96.00%). The confusion matrix revealed that the model accurately predicted the majority of instances in each obesity level category. Feature importance analysis identified weight, height, and gender as the most influential factors in predicting obesity levels, followed by dietary habits, physical activity, and family history of overweight. The model's high accuracy, precision, recall, and F1-score and ability to handle categorical variables effectively make it a valuable tool for obesity risk assessment and classification. The insights gained from the feature importance analysis can guide the development of targeted obesity prevention and management strategies, focusing on modifiable risk factors such as diet and physical activity. While further validation on diverse populations is necessary, the CatBoost model's results demonstrate its potential to support clinical decision-making and inform public health initiatives in the fight against the global obesity epidemic.

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Section: Discussionmentioning

confidence: 99%

Predicting Obesity Levels with High Accuracy: Insights from a CatBoost Machine Learning Model

Maulana,

Afidh,

Maulydia

et al. 2024

Infolitika J. Data Sci.

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Wang,

Misra

2024

Diabetes & Metabolic Syndrome: Clinical Research & Revi

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Combination of Machine Learning Techniques to Predict Overweight/Obesity in Adults

Gutiérrez-Gallego,

Zamorano-León,

Parra-Rodríguez

et al. 2024

JPM

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(1) Background: Artificial intelligence using machine learning techniques may help us to predict and prevent obesity. The aim was to design an interpretable prediction algorithm for overweight/obesity risk based on a combination of different machine learning techniques. (2) Methods: 38 variables related to sociodemographic, lifestyle, and health aspects from 1179 residents in Madrid were collected and used to train predictive models. Accuracy, precision, and recall metrics were tested and compared between nine classical machine learning techniques and the predictive model based on a combination of those classical machine learning techniques. Statistical validation was performed. The shapely additive explanation technique was used to identify the variables with the greatest impact on weight gain. (3) Results: Cascade classifier model combining gradient boosting, random forest, and logistic regression models showed the best predictive results for overweight/obesity compared to all machine learning techniques tested, reaching an accuracy of 79%, precision of 84%, and recall of 89% for predictions for weight gain. Age, sex, academic level, profession, smoking habits, wine consumption, and Mediterranean diet adherence had the highest impact on predicting obesity. (4) Conclusions: A combination of machine learning techniques showed a significant improvement in accuracy to predict risk of overweight/obesity than machine learning techniques separately.

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A review of the application of deep learning in obesity: From early prediction aid to advanced management assistance

Cited by 3 publications

References 73 publications

Predicting Obesity Levels with High Accuracy: Insights from a CatBoost Machine Learning Model

Predicting Obesity Levels with High Accuracy: Insights from a CatBoost Machine Learning Model

Highlights of the current issue

Combination of Machine Learning Techniques to Predict Overweight/Obesity in Adults

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