@article{article_1581396, title={Evaluations of Comet Assay Data Through Statistical Analysis, Machine Learning, and Multi-Criteria Decision-Making Methods for Genotoxic Potential of Food Sweeteners}, journal={Gazi University Journal of Science}, volume={38}, pages={1480–1501}, year={2025}, DOI={10.35378/gujs.1581396}, author={Ünal, Fatma and Özdemir, Muhlis and Yüzbaşıoğlu, Deniz and Mamur, Sevcan and Avuloğlu Yılmaz, Ece and Okuş, Fatma and Akbaş, Ece and Kasap, Reşat and Güneş, Büşra}, keywords={Comet assay, DNA damage, Statistical analysis, Machine learning, Multi-criteria decision-making}, abstract={Although the genotoxic effects of food sweeteners have been studied previously, there is still a lack of application using an integrated approach that combines statistical analysis, Machine Learning (ML), and Multi-Criteria Decision Methods (MCDM) in depth to reveal the DNA-damaging potential of food sweeteners (D-Sorbitol (DS) and Xylitol (XYL)), both alone and in combination (DSX). A dataset on comet assay observations for DNA damage (tail length, tail intensity, and tail moment) was collected from previous studies. Kruskal-Wallis and One-Way ANOVA tests were used to identify significant differences in DNA damage. K-Means and Hierarchical Clustering lead to grouping additives of genotoxic effects, while MOORA and TOPSIS ranked toxicity levels. The findings of MCDM showed that XYL_1000 caused the highest DNA damage (0.726683 and 0.382296). The combination of DS and XYL (DSX_M8) exhibited higher toxicity (0.715258 and 0.37281) compared to their treatments, whereas DSX_M1 revealed the least damaging effect (0.235688 and 0.0324946). This is the first study using this approach. These findings highlight the impact of combining ML and MCDM methods for a more intensive genotoxicity evaluation, providing precious insights into food safety regulations.}, number={3}, publisher={Gazi University}