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Machine Learning-Based Assessment of...

Aslanpour, Dareh.

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

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine Learning-Based Assessment of Obesity: An Investigation of Model Performance and Feature Selection./
作者:	Aslanpour, Dareh.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
面頁冊數:	53 p.
附註:	Source: Masters Abstracts International, Volume: 84-12.
Contained By:	Masters Abstracts International84-12.
標題:	Statistics. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30527694
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. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 53 p.

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

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

This item must not be sold to any third party vendors.

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. 

ISBN: 9798379706371Subjects--Topical Terms:

517247
Statistics.
Subjects--Index Terms:

Machine learning algorithms

Machine Learning-Based Assessment of Obesity: An Investigation of Model Performance and Feature Selection.
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300 $a 53 p.
500 $a Source: Masters Abstracts International, Volume: 84-12.
500 $a Advisor: Wu, Yingnian.
502 $a Thesis (M.S.)--University of California, Los Angeles, 2023.
506 $a This item must not be sold to any third party vendors.
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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650 4 $a Statistics. $3 517247
650 4 $a Biostatistics. $3 1002712
650 4 $a Bioinformatics. $3 553671
653 $a Machine learning algorithms
653 $a Obesity
653 $a Physical conditions
653 $a Prediction
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793 $a English
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