A hybrid approach to obesity level determination with decision tree and pelican optimization algorithm

Nagihan Yağmur

Article Details

A hybrid approach to obesity level determination with decision tree and pelican optimization algorithm

Authors:

Nagihan Yağmur

Publisher:

Kütahya Dumlupinar University

Main Topics:

Machine Learning (Other)

Journal:

Journal of Scientific Reports-A Issue 057, pp.97-109

Document Type:

Article

Publication Date:

6/2024

Document Language:

English

Document ID (Org No.):

2001314810

pISSN:

2687-6167

Keywords:

Abstract:

 Approximately 2 billion people in the world struggle with obesity and factors like eating lifestyle, habits, health conditions and mode of transport affect obesity. In this study, an artificial intelligence and machine learning-based model has been developed to predict obesity levels. It is proposed to create a hybrid model by combining the Decision Tree DT algorithm with the Pelican Optimization Algorithm POA on the obesity dataset of 2111 patients in SSggle. These models emphasize the critical role of parameters, aiming to achieve high performance. To solve the classification problem of multi-class obesity level determination, fuzzy logic-based parameter optimization is used to achieve high performance. While obesity rates are increasing worldwide, the study, which aims to globalize the parameters with the random discovery strategy of POA, is thought to be helpful for health professionals and decision-makers by successfully predicting obesity levels.

 Approximately 2 billion people in the world struggle with obesity and factors like eating lifestyle, habits, health conditions and mode of transport affect obesity. In this study, an artificial intelligence and machine learning-based model has been developed to predict obesity levels. It is proposed to create a hybrid model by combining the Decision Tree DT algorithm with the Pelican Optimization Algorithm POA on the obesity dataset of 2111 patients in SSggle. These models emphasize the critical role of parameters, aiming to achieve high performance. To solve the classification problem of multi-class obesity level determination, fuzzy logic-based parameter optimization is used to achieve high performance. While obesity rates are increasing worldwide, the study, which aims to globalize the parameters with the random discovery strategy of POA, is thought to be helpful for health professionals and decision-makers by successfully predicting obesity levels.

 Approximately 2 billion people in the world struggle with obesity and factors like eating lifestyle, habits, health conditions and mode of transport affect obesity. In this study, an artificial intelligence and machine learning-based model has been developed to predict obesity levels. It is proposed to create a hybrid model by combining the Decision Tree DT algorithm with the Pelican Optimization Algorithm POA on the obesity dataset of 2111 patients in SSggle. These models emphasize the critical role of parameters, aiming to achieve high performance. To solve the classification problem of multi-class obesity level determination, fuzzy logic-based parameter optimization is used to achieve high performance. While obesity rates are increasing worldwide, the study, which aims to globalize the parameters with the random discovery strategy of POA, is thought to be helpful for health professionals and decision-makers by successfully predicting obesity levels.

 Approximately 2 billion people in the world struggle with obesity and factors like eating lifestyle, habits, health conditions and mode of transport affect obesity. In this study, an artificial intelligence and machine learning-based model has been developed to predict obesity levels. It is proposed to create a hybrid model by combining the Decision Tree DT algorithm with the Pelican Optimization Algorithm POA on the obesity dataset of 2111 patients in SSggle. These models emphasize the critical role of parameters, aiming to achieve high performance. To solve the classification problem of multi-class obesity level determination, fuzzy logic-based parameter optimization is used to achieve high performance. While obesity rates are increasing worldwide, the study, which aims to globalize the parameters with the random discovery strategy of POA, is thought to be helpful for health professionals and decision-makers by successfully predicting obesity levels.

Article Details

A hybrid approach to obesity level determination with decision tree and pelican optimization algorithm

Authors:

Nagihan Yağmur

Publisher:

Kütahya Dumlupinar University

Main Topics:

Machine Learning (Other)

Journal:

Journal of Scientific Reports-A Issue 057, pp.97-109

Document Type:

Article

Publication Date:

6/2024

Document Language:

English

Document ID (Org No.):

2001314810

pISSN:

2687-6167

Artificial intelligence

Obesity

Machine learning

Decision tree

Pelican optimization algorithm

Hybrid model

Issues

Article Details

A hybrid approach to obesity level determination with decision tree and pelican optimization algorithm

Authors: Nagihan Yağmur

Publisher: Kütahya Dumlupinar University

Main Topics: Machine Learning (Other)

Journal: Journal of Scientific Reports-A Issue 057, pp.97-109

Document Type: Article

Publication Date: 6/2024

Document Language: English

Document ID (Org No.): 2001314810

pISSN: 2687-6167

Artificial intelligence

Obesity

Machine learning

Decision tree

Pelican optimization algorithm

Hybrid model

Issues

Authors:

Nagihan Yağmur

Publisher:

Kütahya Dumlupinar University

Main Topics:

Machine Learning (Other)

Journal:

Journal of Scientific Reports-A Issue 057, pp.97-109

Document Type:

Article

Publication Date:

6/2024

Document Language:

English

Document ID (Org No.):

2001314810

pISSN:

2687-6167