Assessing the efficiency of the cancer detection algorithms using synthetic data based on machine learning

This study analyzes the distribution of real objects and synthetically augmented classes, as well as their impact on machine learning models. The training results of logistic regression, decision trees, random forest, and SVM models on synthetic data were compared with those obtained on a dataset of real objects. Experimental results showe that the use of synthetically augmented data improves the accuracy of classification models, with particularly noticeable improvements observed in some algorithms.

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