sapiСистемный анализ и прикладная информатика«System analysis and applied information science»2309-49232414-0481Belarusian National Technical University10.21122/2309-4923-2025-2-46-53sapi-747Research ArticleОбработка информации и принятие решенийData processing and decision–makingОценка эффективности алгоритмов выявления рака с использованием синтетических данных на основе машинного обученияAssessing the efficiency of the cancer detection algorithms using synthetic data based on machine learningХайдаровШ. И.KhaydarovSh. I.

Хайдаров Шерали Исламович – преподаватель кафедры информационных технологий.

 

Sherali Islomovich Khaydarov – Lecturer at the Department of Information Technologies.

 

sh.haydarov@dtpi.uzДенауский институт предпринимательства и педагогики (DTPI)УзбекистанDenau Institute of Entrepreneurship and Pedagogy (DTPI)Uzbekistan202515082025024653Copyright © Хайдаров Ш.И., 20252025Хайдаров Ш.И.Khaydarov S.I.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://sapi.bntu.by/jour/article/view/747

В данном исследовании проанализировано распределение реальных объектов и синтетически расширенных данных, а также оценено их влияние на модели машинного обучения. Были сопоставлены результаты обучения моделей: Logistic Regression, Decision Tree, Random Forest и SVM на синтетических данных с результатами, полученными на датасете реальных объектов. Экспериментальные результаты показали, что использование синтетически расширенных данных способствует повышению точности классификационной модели, причем особенно заметное улучшение наблюдается в некоторых алгоритмах.

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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The authors declare that there are no conflicts of interest present.