Comparison of Clustering Methods for Obesity Classification

Ahn, Sung Hee; Wang, C.; Shin, Gee Won; Park, Daeseong; Kang, Yong-Cheol; Joibi, Jaramier Cobrado; Yun, Mira

doi:10.1109/ieem.2018.8607277

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…When compared to other techniques, the researchers discovered that the Fuzzy Rule-Based System (FRBS) is more accurate. It also means that FRBS's performance is precise [13].…”

Section: Related Workmentioning

confidence: 99%

Application of Multiple Machine Learning Techniques in Classifying Obesity Level Using Multivariate Dataset

Lawas

2023

ijarcs

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People's health is important. It must be preserved. There are a variety of issues that might contribute to a person's health, including their lifestyle. One of the most prominent concerns during this pandemic is a person's body weight, which is particularly significant these days. Obesity is one of those body diseases about which it is important to be aware of the reasons. This study intends to create different machine learning models to define what causes obesity, select the most appropriate model that did the best, and discuss how accurate it performed. It can also be used in determining the how the obesity impact in our daily lives. Through the use of different machine learning models such as KNN, Random Forest, Gradient Boosting and Ada Boost, the study be able to obtain the appropriate model. Despite being trained on unbalanced data, the classifiers utilized were able to predict the properties of the presented datasets that Random Forest has an accuracy of 83.6%.

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“…When compared to other techniques, the researchers discovered that the Fuzzy Rule-Based System (FRBS) is more accurate. It also means that FRBS's performance is precise [13].…”

Section: Related Workmentioning

confidence: 99%

Application of Multiple Machine Learning Techniques in Classifying Obesity Level Using Multivariate Dataset

Lawas

2023

ijarcs

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“…Ahn et al [10] formulated a system to foretell obesity using Artificial Intelligence techniques. They have come to a conclusion that systems based on fuzzy rule is the best model to classify obesity [10].…”

Section: Related Workmentioning

confidence: 99%

Utilizing the Data Mining Techniques for Obesity Prognosis Based on Eating and Lifestyle Routines of Adolescents and Adults

Sankar

Sreekumar

2021

Advanced Computing and Intelligent Technologies

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Since 1980, obesity has increasingly become a global health issue-it is increasingly prevalent among children with serious implications for their health and their later well-being as adults. The World Health Organization considers obesity a non-communicable disease which may lead to severe diseases such as diabetes, cardiovascular diseases, and various kinds of cancers. Detrimental dietary patterns and the absence of physical activity are the root causes of obesity. Data mining is a quickly developing field with numerous applications in the field of medical services. One of its several utilizations is the forecasting of sicknesses. The proposed work intends to examine varied machine learning techniques: K-Nearest Neighbor, Decision Tree, Ensemble Random Forest, and Deep Neural Network to prognosticate obesity. The inference analysis shows that the ensemble random forest classification with XG Boosting is the most precise procedure to prognosis obesity.

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Comparison of Classification Methods using Historical Loan Application Data

Kelen

Emanuel

2019

2019 4th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE)

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Comparison of Clustering Methods for Obesity Classification

Cited by 4 publications

References 14 publications

Application of Multiple Machine Learning Techniques in Classifying Obesity Level Using Multivariate Dataset

Application of Multiple Machine Learning Techniques in Classifying Obesity Level Using Multivariate Dataset

Utilizing the Data Mining Techniques for Obesity Prognosis Based on Eating and Lifestyle Routines of Adolescents and Adults

Comparison of Classification Methods using Historical Loan Application Data

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