Classification and prediction of obesity levels among subjects in Colombia, Peru, and Mexico using unsupervised and supervised learning
This research investigates the multifaceted relationship between various factors and obesity rates in Mexico, Peru, and Colombia using a publicly available dataset. Through Python, the study employs classification and clustering analyses, focusing on logistic regression to predict obesity levels a...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Zenodo
2024
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| Online Access: | http://umpir.ump.edu.my/id/eprint/42159/ http://umpir.ump.edu.my/id/eprint/42159/1/Classification%20and%20Prediction%20of%20Obesity%20Levels%20among%20Subjects%20in%20Colombia%2C%20Peru%2C%20and%20Mexico%20Using%20Unsupervised%20and%20Supervised%20Learning.pdf |
| _version_ | 1848826535776092160 |
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| author | Suhaila, Bahrom Anuar, Ab Rani Aisyah Amalina, Mohd Noor |
| author_facet | Suhaila, Bahrom Anuar, Ab Rani Aisyah Amalina, Mohd Noor |
| author_sort | Suhaila, Bahrom |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | This research investigates the multifaceted relationship between various factors and obesity rates in
Mexico, Peru, and Colombia using a publicly available dataset. Through Python, the study employs classification
and clustering analyses, focusing on logistic regression to predict obesity levels and generate actionable
recommendations. Combining exploratory data analysis (EDA) and advanced machine learning techniques, the
research aims to unveil nuanced insights into obesity determinants. Unsupervised learning methods segmentize
individuals, providing deeper insights into obesity profiles. Supervised learning algorithms like logistic regression,
random forest, and adaboost classifier predict obesity levels based on labelled datasets, with random forest exhibiting
superior performance. The study enhances understanding of obesity classification through machine learning and
integrates data inspection, formatting, and exploration using Excel, Python, and graphical user interfaces (GUIs)
such as SweetViz and PandaGui. Overall, it offers a comprehensive approach to understanding and addressing
obesity using sophisticated analytical tools and methodologies. |
| first_indexed | 2025-11-15T03:46:22Z |
| format | Article |
| id | ump-42159 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:46:22Z |
| publishDate | 2024 |
| publisher | Zenodo |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-421592024-08-05T03:45:52Z http://umpir.ump.edu.my/id/eprint/42159/ Classification and prediction of obesity levels among subjects in Colombia, Peru, and Mexico using unsupervised and supervised learning Suhaila, Bahrom Anuar, Ab Rani Aisyah Amalina, Mohd Noor QA Mathematics QA76 Computer software This research investigates the multifaceted relationship between various factors and obesity rates in Mexico, Peru, and Colombia using a publicly available dataset. Through Python, the study employs classification and clustering analyses, focusing on logistic regression to predict obesity levels and generate actionable recommendations. Combining exploratory data analysis (EDA) and advanced machine learning techniques, the research aims to unveil nuanced insights into obesity determinants. Unsupervised learning methods segmentize individuals, providing deeper insights into obesity profiles. Supervised learning algorithms like logistic regression, random forest, and adaboost classifier predict obesity levels based on labelled datasets, with random forest exhibiting superior performance. The study enhances understanding of obesity classification through machine learning and integrates data inspection, formatting, and exploration using Excel, Python, and graphical user interfaces (GUIs) such as SweetViz and PandaGui. Overall, it offers a comprehensive approach to understanding and addressing obesity using sophisticated analytical tools and methodologies. Zenodo 2024 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/42159/1/Classification%20and%20Prediction%20of%20Obesity%20Levels%20among%20Subjects%20in%20Colombia%2C%20Peru%2C%20and%20Mexico%20Using%20Unsupervised%20and%20Supervised%20Learning.pdf Suhaila, Bahrom and Anuar, Ab Rani and Aisyah Amalina, Mohd Noor (2024) Classification and prediction of obesity levels among subjects in Colombia, Peru, and Mexico using unsupervised and supervised learning. APS Proceedings, 13. 29-36.. (Published) https://doi.org/10.5281/zenodo.12791087 10.5281/zenodo.12791087 |
| spellingShingle | QA Mathematics QA76 Computer software Suhaila, Bahrom Anuar, Ab Rani Aisyah Amalina, Mohd Noor Classification and prediction of obesity levels among subjects in Colombia, Peru, and Mexico using unsupervised and supervised learning |
| title | Classification and prediction of obesity levels among subjects in Colombia, Peru, and Mexico using unsupervised and supervised learning |
| title_full | Classification and prediction of obesity levels among subjects in Colombia, Peru, and Mexico using unsupervised and supervised learning |
| title_fullStr | Classification and prediction of obesity levels among subjects in Colombia, Peru, and Mexico using unsupervised and supervised learning |
| title_full_unstemmed | Classification and prediction of obesity levels among subjects in Colombia, Peru, and Mexico using unsupervised and supervised learning |
| title_short | Classification and prediction of obesity levels among subjects in Colombia, Peru, and Mexico using unsupervised and supervised learning |
| title_sort | classification and prediction of obesity levels among subjects in colombia, peru, and mexico using unsupervised and supervised learning |
| topic | QA Mathematics QA76 Computer software |
| url | http://umpir.ump.edu.my/id/eprint/42159/ http://umpir.ump.edu.my/id/eprint/42159/ http://umpir.ump.edu.my/id/eprint/42159/ http://umpir.ump.edu.my/id/eprint/42159/1/Classification%20and%20Prediction%20of%20Obesity%20Levels%20among%20Subjects%20in%20Colombia%2C%20Peru%2C%20and%20Mexico%20Using%20Unsupervised%20and%20Supervised%20Learning.pdf |