hfbHealth, Food & BiotechnologyHealth, Food & Biotechnology2712-7648РОСБИОТЕХ10.36107/hfb.2019.i3.s238hfb-8Research ArticleЗДОРОВЬЕHEALTHСравнение возможностей логистической регрессии и искусственных нейронных сетей в прогнозировании результатов исследования на малой выборкеComparison of the Possibilities of Logistic Regression and Artificial Neural Networks in Predicting the Results of Research on f Small SampleБазылевВ. В.BazylevV. V.

Базылев Владлен Владленович

440071, город Пенза, ул. Стасова, дом 6 

Vladlen V. Bazylev

6, Stasova str., Penza, 440071 

cardio58@yandex.ruКарнахинВ. А.KarnakhinV. A.

Карнахин Вадим Александрович

440071, город Пенза, ул. Стасова, дом 6 

Vadim A. Karnakhin

6, Stasova str., Penza, 440071 

vkhin@mail.ruФедеральный центр сердечно-сосудистой хирургии Министерства здравоохранения РоссииРоссияFederal Center Cardiovascular SurgeryRussian Federation201904052021131120Copyright © Базылев В.В., Карнахин В.А., 20212021Базылев В.В., Карнахин В.А.Bazylev V.V., Karnakhin V.A.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.hfb-journal.ru/jour/article/view/8

В настоящее время все чаще предпринимаются попытки сравнивать различные количественные модели для решения конкретных задач классификации данных. При этом в литературе отсутствуют данные о сравнении математических моделей в условиях малых выборок и сложных клинических ситуаций. Цель работы. Сравнить производительность моделей искусственных нейронных сетей и логистической регрессии в прогнозировании результатов исследования в условиях малой выборки. Материалы и методы. В симуляцию включена группа больных из 50 человек, которым была выполнена пластическая операция на митральном клапане. Для симуляции выбраны пять независимых переменных: пол, возраст, индекс массы тела, методика аппроксимации папиллярных мышц. Зависимая переменная - регургитация на митральном клапане в отдаленном периоде. Результаты. По данным логистической регрессии возник феномен разделения данных и получена огромная среднеквадратичная ошибка. По результатам анализа ROC-кривой выявлена зависимость между предиктором возраст и регургитацией на митральном клапане, площадь под кривой говорит о среднем уровне взаимосвязи. Результаты анализа предикторов с помощью искусственных нейронных сетей указывают на то, что основной вклад в качестве предиктора отсутствия регургитации оказывает процедура аппроксимации папиллярных мышц. С помощью теста De-Long проведено сравнение ROC-кривых регрессии и нейронных сетей по фактору возраст: z = 10.71, p <0,0001, выявлены статистически значимые различия, что говорит о преимуществе ИСН в выявлении предикторов. Заключение. При малой выборке с небольшим количеством событий искусственные нейронные сети имеют преимущество над другими методиками при определении предикторов влияния на зависимую переменную.

Currently, attempts are increasingly being made to compare various quantitative models to solve specific data classification problems. Moreover, in the literature there is no data on the comparison of mathematical models in small samples and complex clinical situations. Purpose of work. Compare the performance of artificial neural network models and logistic regression in predicting research results in a small sample. Materials and methods. The simulation included a group of patients of 50 people who underwent plastic surgery on the mitral valve. Five independent variables were selected for the simulation: gender, age, body mass index, and papillary muscle approximation technique. The dependent variable is regurgitation on the mitral valve in a distant period. Results. According to the logistic regression, a phenomenon of data separation arose and a huge mean square error was obtained. According to the analysis of the ROC curve, a relationship was revealed between the predictor age and regurgitation on the mitral valve, the area under the curve indicates the average level of relationship. The results of the analysis of predictors using artificial neural networks indicate that the main contribution as a predictor of the absence of regurgitation is made by the approximation of papillary muscles. Using the De-Long test, we compared the ROC regression curves and neural networks by age factor: z = 10.71, p <0.0001, statistically significant differences were revealed, which indicates the advantage of STI in identifying predictors. Conclusion. In a small sample with a small number of events, artificial neural networks have an advantage over other methods in determining predictors of influence on the dependent variable.

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