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Machine Learning-Based Assessment of Obesity : = An Investigation of Model Performance and Feature Selection.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Machine Learning-Based Assessment of Obesity :/
Reminder of title:	An Investigation of Model Performance and Feature Selection.
Author:	Aslanpour, Dareh.
Description:	1 online resource (53 pages)
Notes:	Source: Masters Abstracts International, Volume: 84-12.
Contained By:	Masters Abstracts International84-12.
Subject:	Statistics. -
Online resource:	click for full text (PQDT)
ISBN:	9798379706371

Machine Learning-Based Assessment of Obesity : = An Investigation of Model Performance and Feature Selection.
Aslanpour, Dareh.

Machine Learning-Based Assessment of Obesity :An Investigation of Model Performance and Feature Selection. - 1 online resource (53 pages)

Source: Masters Abstracts International, Volume: 84-12.

Thesis (M.S.)--University of California, Los Angeles, 2023.

Includes bibliographical references

The objective of this paper is to employ various machine learning algorithms to investigate the assessment of obesity levels based on eating habits and physical conditions. The study will utilize the obesity level estimation data provided by UCI Machine Learning Repository. The performance of different model candidates will be evaluated and compared in order to select the most robust model for obesity estimation or prediction. Moreover, this research aims to identify the crucial features used in the best predictive model to enhance the accuracy of obesity prediction. This study intends to contribute to the ongoing research in the field of machine learning and healthcare by providing insights into the prediction of obesity.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798379706371Subjects--Topical Terms:

556824
Statistics.
Subjects--Index Terms:

Machine learning algorithmsIndex Terms--Genre/Form:

554714
Electronic books.

Machine Learning-Based Assessment of Obesity : = An Investigation of Model Performance and Feature Selection.
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520 $a The objective of this paper is to employ various machine learning algorithms to investigate the assessment of obesity levels based on eating habits and physical conditions. The study will utilize the obesity level estimation data provided by UCI Machine Learning Repository. The performance of different model candidates will be evaluated and compared in order to select the most robust model for obesity estimation or prediction. Moreover, this research aims to identify the crucial features used in the best predictive model to enhance the accuracy of obesity prediction. This study intends to contribute to the ongoing research in the field of machine learning and healthcare by providing insights into the prediction of obesity.
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653 $a Physical conditions
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