ML has gained popularity in cancer studies and biomedical data analysis for identifying patterns. In our study, we used a final dataset comprising 40 metabolites, with 20 selected from a previous study 1 and 20 randomly selected from a HMDB. The filtered dataset consisted of 31 attributes. We employed various classifiers, such as SGD, Random Forest, Hoeffding Tree, and more, to evaluate the accuracy of classification.