Predicting obesity: a comparative analysis of machine learning models incorporating different features
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023.
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| Idioma: | English |
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Brac University
2023
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| Acesso em linha: | http://hdl.handle.net/10361/21929 |
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10361-21929 |
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10361-219292023-12-06T21:02:28Z Predicting obesity: a comparative analysis of machine learning models incorporating different features Rahman, Md.Sakibur Ahmed, Kaosar Nafis, Tanvir Alam Hossain, Md. Ridwan Majumder, Swapnil Sadeque, Farig Yousuf Department of Computer Science and Engineering, Brac University Supervision Unsustainable lifestyle AI system Self monitoring Pre-existing diseases Machine learning Artificial intelligence This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023. Cataloged from PDF version of thesis. Includes bibliographical references (pages 47-48). Obesity, the excessive accumulation of body fat, is a significant health risk associated with various detrimental impacts, including the development of chronic diseases, metabolic abnormalities, joint problems, sleep apnea, mental health issues, repro- ductive health difficulties, respiratory disorders, liver disease, and surgical risks. The emergence of machine learning, which offers potent analytical tools and high- performance computing capabilities, has revolutionised the interdisciplinary health industry. Through improved understanding and therapeutic interventions, this tech- nology offers opportunities to address and overcome the severe harm that obesity causes. This thesis aims to develop an automated system that utilises machine learning techniques to predict obesity based on different eating habits and relevant features. A comprehensive research methodology will be presented to categorise risk factors associated with an unhealthy lifestyle using machine learning. To effectively handle and anticipate various types of obesity, our AI system will analyse user data, including height, weight, daily food consumption habits, and more. The system will consider both weight-related and non-weight-related variables, as well as other fea- tures, to provide comprehensive insights into this health condition. Additionally, our technology will assist individuals by accurately classifying different forms of obesity, such as overweight I, overweight II, and beyond. Coefficient and correlation matri- ces have been utilised in the analysis to further enhance predictability. Therefore, by employing our obesity prediction algorithm, individuals can obtain estimates re- garding various levels of obesity. Empowered with this information, individuals can actively improve their health status by modifying their eating habits in accordance with their specific obesity condition. The primary objective of this research is to include and exclude features associated with predicting different levels of obesity and to see how this affects the accuracy scores. A secondary dataset and a range of machine learning techniques were employed to accomplish this goal, resulting in improved predictability and accuracy of the obesity-related outcomes. Md.Sakibur Rahman Kaosar Ahmed Tanvir Alam Nafis Md. Ridwan Hossain Swapnil Majumder B.Sc. in Computer Science and Engineering 2023-12-06T05:46:28Z 2023-12-06T05:46:28Z 2023 2023-05 Thesis ID 19101319 ID 19101328 ID 19101575 ID 19101305 ID 19101572 http://hdl.handle.net/10361/21929 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 48 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Supervision Unsustainable lifestyle AI system Self monitoring Pre-existing diseases Machine learning Artificial intelligence |
| spellingShingle |
Supervision Unsustainable lifestyle AI system Self monitoring Pre-existing diseases Machine learning Artificial intelligence Rahman, Md.Sakibur Ahmed, Kaosar Nafis, Tanvir Alam Hossain, Md. Ridwan Majumder, Swapnil Predicting obesity: a comparative analysis of machine learning models incorporating different features |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023. |
| author2 |
Sadeque, Farig Yousuf |
| author_facet |
Sadeque, Farig Yousuf Rahman, Md.Sakibur Ahmed, Kaosar Nafis, Tanvir Alam Hossain, Md. Ridwan Majumder, Swapnil |
| format |
Thesis |
| author |
Rahman, Md.Sakibur Ahmed, Kaosar Nafis, Tanvir Alam Hossain, Md. Ridwan Majumder, Swapnil |
| author_sort |
Rahman, Md.Sakibur |
| title |
Predicting obesity: a comparative analysis of machine learning models incorporating different features |
| title_short |
Predicting obesity: a comparative analysis of machine learning models incorporating different features |
| title_full |
Predicting obesity: a comparative analysis of machine learning models incorporating different features |
| title_fullStr |
Predicting obesity: a comparative analysis of machine learning models incorporating different features |
| title_full_unstemmed |
Predicting obesity: a comparative analysis of machine learning models incorporating different features |
| title_sort |
predicting obesity: a comparative analysis of machine learning models incorporating different features |
| publisher |
Brac University |
| publishDate |
2023 |
| url |
http://hdl.handle.net/10361/21929 |
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