Using Machine Learning for Personalized Patient Adherence Level Determination

Maksim Taranik, Georgy Kopanitsa

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

The paper deals with using a machine-learning algorithm for patient adherence level determination. For this purpose, we developed a neural network using the Python language, Keras library, and PyCharm platform. We analyzed different medical data collected from medical staff, patient interviews, and measurements preprocessed using a fuzzy Mamdani algorithm. After analysing 369 records we received 79.4% of accuracy.

Original languageEnglish
Pages (from-to)174-178
Number of pages5
JournalStudies in Health Technology and Informatics
Volume261
Publication statusPublished - 1 Jan 2019
Externally publishedYes

Keywords

  • Adherence
  • fuzzy logic
  • Keras
  • machine learning

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management

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