Comparison of Two-Criterion Evolutionary Filtering Techniques in Cardiovascular Predictive Modelling : доклад, тезисы доклада | Научно-инновационный портал СФУ

Comparison of Two-Criterion Evolutionary Filtering Techniques in Cardiovascular Predictive Modelling : доклад, тезисы доклада

Тип публикации: доклад, тезисы доклада, статья из сборника материалов конференций

Конференция: International Conference on Informatics in Control, Automation and Robotics; Lisbon, Portugal; Lisbon, Portugal

Год издания: 2016

Ключевые слова: feature selection, Two-Criterion Filtering, Cooperative Multi-Objective Genetic Algorithm, Cardiovascular Modelling

Аннотация: In this paper we compare a number of two-criterion filtering techniques for feature selection in?cardiovascular predictive modelling. We design two-objective schemes based on different combinations of four criteria describing the quality of reduced feature sets. To find attribute subsystems meeting the?introduced criteria in an optimal way, we suggest applying a cooperative multi-objective genetic algorithm. It includes various search strategies working in a parallel way, which allows additional experiments to be avoided when choosing the most effective heuristic for the problem considered. The performance of filtering techniques was investigated in combination with the SVM model on a population-based epidemiological database called KIHD (Kuopio Ischemic Heart Disease Risk Factor Study). The dataset consists of a large number of variables on various characteristics of the study participants. These baseline measures were collected at the beginning of the study. In addition, all major cardiovascular events that had occurred among the participants over an average of 27 years of follow-up were collected from the national health registries. As a result, we found that the usage of the filtering technique including intra- and interclass distances led to a significant reduction of the feature set (up to 11 times, from 433 to 38 features) without detriment to the predictive ability of the SVM model. This implies that there is a possibility to cut down on the clinical tests needed to collect the data, which is relevant to the prediction of cardiovascular diseases.

Ссылки на полный текст

Издание

Журнал: ICINCO 2016 - Proceedings of the 13th International Conference on Informatics in Control, Automation and Robotics

Выпуск журнала: 1

Номера страниц: 140-145

Издатель: SCITEPRESS

Авторы

  • Brester C.
  • Kauhanen J.
  • Tuomainen T-P.
  • Semenkin E. (Siberian State Aerospace University)
  • Kolehmainen M.
  • Oleg Gusikhin; Dimitri Peaucelle; Kurosh Madani

Вхождение в базы данных

Информация о публикациях загружается с сайта службы поддержки публикационной активности СФУ. Сообщите, если заметили неточности.

Вы можете отметить интересные фрагменты текста, которые будут доступны по уникальной ссылке в адресной строке браузера.