Machine Learning Based Adult Obesity Prediction

Devi, K.Nirmala; Krishnamoorthy, N.; Jayanthi, P.; Karthi, S.; Karthik, T.; Kiranbharath, K.

doi:10.1109/iccci54379.2022.9740995

Cited by 13 publications

(7 citation statements)

References 8 publications

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“…Furthermore, in study [27] predicted obesity in adults using their dietary patterns and various ML techniques, with logistic regression and DT models being the most relevant for predicting obesity. Finally, the results obtained in study [30] show similar levels of accuracy, where models such as SVM, DT, neural networks, and PCA were used to find that a decisive factor in obesity is family history, reaching an accuracy rate of 90% for DT and neural networks.…”

Section: Discussionmentioning

confidence: 99%

“…The study also showed that individuals with allelic variants AgRP, Ala67Ala, ADRB2, Gln27Glu, Glu27Glu, INSIG2, Ala12Ala, and Pro 12 pro tend to develop obesity. Also, in [27], a predictive model was created to predict obesity in adult populations using ML techniques such as LR, Random Forest, Decision Tree, SVM, Gradient Boost, and Ada Boost. The study showed that LR and Decision Tree had the best performance in predicting obesity in adults based on accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

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Predicting Obesity in Nutritional Patients using Decision Tree Modeling

Iparraguirre-Villanueva,

Mirano-Portilla,

Gamarra-Mendoza

et al. 2024

IJACSA

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Obesity has become a widespread problem that affects not only physical well-being but also mental health. To address this problem and provide solutions, Machine Learning (ML) technology tools are being applied. Studies are currently being developed to improve the prediction of obesity. This study aimed to predict obesity levels in nutritional patients by analyzing their physical and dietary habits using the Decision Tree (DT) model. For the development of this work, we chose to use the CRISP-DM framework to follow the development in an organized way, thus achieving a better understanding of the data and describing, evaluating, and analyzing the results. The results of this work yielded metrics with significant values for predicting obesity: so much so that the accuracy rate was 92.89%, the sensitivity rate was 94% and the F1 score was 93%. Likewise, accuracy metrics above 88% were obtained for each level of obesity, demonstrating the effectiveness of the DT model in predicting this type of task. Finally, the results demonstrate that the DT model is effective in predicting obesity, with significant results that motivate further research to continue improving accuracy in this type of task.

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Section: Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Predicting Obesity in Nutritional Patients using Decision Tree Modeling

Iparraguirre-Villanueva,

Mirano-Portilla,

Gamarra-Mendoza

et al. 2024

IJACSA

View full text Add to dashboard Cite

show abstract

“…Family history of obesity emerges as a significant feature, emphasizing genetic or dietary influences [4] .This research addresses obesity prediction through machine learning algorithms, including Logistic Regression, Random Forest, and Ensemble techniques like Ada Boost and Voting Classifier. The Logistic Regression model exhibits the highest accuracy, emphasizing its effectiveness in predicting obesity levels [5] .Focusing on the rising global epidemic of obesity, the study employs machine learning methods to predict obesity risk. The Gradient Boosting technique outperforms other classifiers, achieving an accuracy of 97.08%.…”

Section: Literature Reviewmentioning

confidence: 99%

Automated Obesity Detection and Classification via Live Camera Analysis

2023

IRJMETS

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Nearly 2 billion people in the world are fighting against "Corpulence" means in simple words we can say that they are facing a type of chronic diseases called "obesity or fatness." Not only the Obesity but also malnourished Underweight and Overweight are the today's concern. This problem is for all people including all genders and age. According to WHO the fat people in the world 2023 out of 2 billion people are : 650 million adults, 340 million adolescents, and 39 million children. Likewise, about 3-5% of adult population fall under a category of Underweight. Obesity causes the risk of hazardous diseases like Type 2 diabetes, Stroke, heart disease, etc. Underweight people can have health problems like Anemia, Osteoporosis, Feeling tired all the time, etc. Hence by considering all the situations we decided to help them out. We are using dataset which is collected from Kaggle contains the features like Age, Gender, Height, Weight, and BMI, etc. We are using input Dataset as well as Live Camera to become a user convenient. We are using algorithms like SVM, Random Forest, Decision Tree for dataset as input. Likewise, we are using Logistic Regression for live camera as input. All these algorithms will be used in training to check accuracy, precision, recall, f1_ score, etc. The algorithm which is more efficient will be used for testing.

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“…Methodologies used in the base paper are R-CNN, YOLO -V4. We have implemented a concept using VGG19 which is 19 deep layers [10] [11]. A pre-trained version of the network can be loaded using the ImageNet database, which has been trained on over a million images.…”

Section: Proposed Workmentioning

confidence: 99%

Scalp disease analysis using deep learning models

Krishnamoorthy,

Jayanthi,

Kumaravel

et al. 2023

ACE

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Alopecia areata, folliculitis, hair loss, and dandruff are common scalp hair disorders caused by nutritional imbalances, stress, and pollution in the environment. Specialized treatments, such as hair physiotherapy, have arisen to address this issue. This paper pro-poses a deep learning model that can analysis the scalp diseases with high accuracy, using this model we can predict the scalp diseases which makes easier for them to treat the dis-ease with good treatment using the mobile phones we can predict the diseases simply us-ing its image. This paper deals with an advanced classification model which predicts scalp disease. More than 1100 images are used as training dataset, CNN is used for the classifi-cation and the high accuracy is achieved using VGG16, VGG19 and MobileNetV2.

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Machine Learning Based Adult Obesity Prediction

Cited by 13 publications

References 8 publications

Predicting Obesity in Nutritional Patients using Decision Tree Modeling

Predicting Obesity in Nutritional Patients using Decision Tree Modeling

Automated Obesity Detection and Classification via Live Camera Analysis

Scalp disease analysis using deep learning models

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