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

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.