EconPapers    
Economics at your fingertips  
 

Analysis of obesity statuses on rats using blood test parameters: A feasibility study

Ali Berkan Ural () and Evren Koç ()

Edelweiss Applied Science and Technology, 2025, vol. 9, issue 3, 1665-1674

Abstract: Although obesity has become a significant issue in our era, various diagnostic systems and kits are being developed for its early detection. In addition to numerous studies in the literature, the early diagnosis of obesity has generally been conducted on human participants. This study introduces an innovative feasibility approach by developing an AI and machine learning-based obesity prediction and interpretation application using blood test parameters obtained from rodent subjects. In the experimental phase, with a publicly available dataset, 10 obese and 10 normal (control group) rats were selected, ensuring a meaningful and appropriate sample size for veterinary research. Specific blood test parameters of these subjects were analyzed. These parameters were compiled into a data form and subjected to machine learning-based prediction and interpretation. The machine learning methods used in this study included k-Nearest Neighbors (k-NN), Support Vector Machine (SVM), and Random Forest algorithms. Performance analyses were conducted for each method based on the obtained results. The highest accuracy rate was achieved with the Random Forest algorithm, reaching approximately 97.4%. The accuracy rates obtained with other models were also significant, demonstrating that the study has the potential to be further developed and applied to other living beings, including humans.

Keywords: Computer Aided Systems; Feature Selection; Machine Learning Models; Obesity; Rats. (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://learning-gate.com/index.php/2576-8484/article/view/5666/2023 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ajp:edwast:v:9:y:2025:i:3:p:1665-1674:id:5666

Access Statistics for this article

More articles in Edelweiss Applied Science and Technology from Learning Gate
Bibliographic data for series maintained by Melissa Fernandes ().

 
Page updated 2025-03-24
Handle: RePEc:ajp:edwast:v:9:y:2025:i:3:p:1665-1674:id:5666