A Comparative Study: Classification Vs. Matrix Factorization for Therapeutics Recommendation

Seda Polat Erdeniz*, Michael Schrempf, Diether Kramer, Alexander Felfernig

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review


Hospital information systems (HIS) hold various healthcare information of patients. Most of them are held as structural data by a database table. This information include history of diagnoses, medications, applied procedures and laboratory results of patients which can be used by machine learning methods to predict some useful information about patients. These predictions can be the progress of a disease, which is called prognosis, or it can also be therapeutics which includes medications and procedures. In this paper, we explain how to recommend therapeutics using various machine learning approaches, especially by comparing classification methods with matrix factorization (a recommender systems approach). In order to evaluate the performance of compared methods, we applied experiments on real patients' electronic health records (EHR). We observed that matrix factorization outperforms the compared classification approaches in terms of accuracy. Therefore, it is feasible to employ matrix factorization in clinical decision support systems to provide therapeutics recommendations which improves the daily performance of physicians, so the life quality of the patients.
Original languageEnglish
Title of host publicationFoundations of Intelligent Systems - 26th International Symposium, ISMIS 2022, Proceedings
EditorsMichelangelo Ceci, Sergio Flesca, Elio Masciari, Giuseppe Manco, Zbigniew W. Raś
Place of PublicationCham
PublisherSpringer International Publishing AG
Number of pages10
ISBN (Print)9783031165634
Publication statusPublished - 26 Sep 2022
Event26th International Symposium on Methodologies for Intelligent Systems: ISMIS 2022 - Cosenza, Cosenza, Italy
Duration: 3 Oct 20225 Oct 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13515 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference26th International Symposium on Methodologies for Intelligent Systems
Abbreviated titleISMIS 2022
Internet address


  • Healthcare
  • Matrix factorization
  • Multi-label classification
  • Recommender systems

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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