PMID- 21818444
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20111110
LR  - 20220310
IS  - 2093-369X (Electronic)
IS  - 2093-3681 (Print)
IS  - 2093-3681 (Linking)
VI  - 16
IP  - 4
DP  - 2010 Dec
TI  - Application of support vector machine for prediction of medication adherence in 
      heart failure patients.
PG  - 253-9
LID - 10.4258/hir.2010.16.4.253 [doi]
AB  - OBJECTIVES: Heart failure (HF) is a progressive syndrome that marks the end-stage 
      of heart diseases, and it has a high mortality rate and significant cost burden. 
      In particular, non-adherence of medication in HF patients may result in serious 
      consequences such as hospital readmission and death. This study aims to identify 
      predictors of medication adherence in HF patients. In this work, we applied a 
      Support Vector Machine (SVM), a machine-learning method useful for data 
      classification. METHODS: Data about medication adherence were collected from 
      patients at a university hospital through self-reported questionnaire. The data 
      included 11 variables of 76 patients with HF. Mathematical simulations were 
      conducted in order to develop a SVM model for the identification of variables 
      that would best predict medication adherence. To evaluate the robustness of the 
      estimates made with the SVM models, leave-one-out cross-validation (LOOCV) was 
      conducted on the data set. RESULTS: THE TWO MODELS THAT BEST CLASSIFIED 
      MEDICATION ADHERENCE IN THE HF PATIENTS WERE: one with five predictors (gender, 
      daily frequency of medication, medication knowledge, New York Heart Association 
      [NYHA] functional class, spouse) and the other with seven predictors (age, 
      education, monthly income, ejection fraction, Mini-Mental Status 
      Examination-Korean [MMSE-K], medication knowledge, NYHA functional class). The 
      highest detection accuracy was 77.63%. CONCLUSIONS: SVM modeling is a promising 
      classification approach for predicting medication adherence in HF patients. This 
      predictive model helps stratify the patients so that evidence-based decisions can 
      be made and patients managed appropriately. Further, this approach should be 
      further explored in other complex diseases using other common variables.
FAU - Son, Youn-Jung
AU  - Son YJ
AD  - Department of Nursing, Soonchunhyang University, Cheonan, Korea.
FAU - Kim, Hong-Gee
AU  - Kim HG
FAU - Kim, Eung-Hee
AU  - Kim EH
FAU - Choi, Sangsup
AU  - Choi S
FAU - Lee, Soo-Kyoung
AU  - Lee SK
LA  - eng
PT  - Journal Article
DEP - 20101231
PL  - Korea (South)
TA  - Healthc Inform Res
JT  - Healthcare informatics research
JID - 101534553
PMC - PMC3092139
OTO - NOTNLM
OT  - Heart Failure
OT  - Medication
OT  - Patient Adherence
OT  - Support Vector Machine
COIS- No potential conflict of interest relevant to this article was reported.
EDAT- 2011/08/06 06:00
MHDA- 2011/08/06 06:01
PMCR- 2010/12/01
CRDT- 2011/08/06 06:00
PHST- 2010/08/19 00:00 [received]
PHST- 2010/09/14 00:00 [accepted]
PHST- 2011/08/06 06:00 [entrez]
PHST- 2011/08/06 06:00 [pubmed]
PHST- 2011/08/06 06:01 [medline]
PHST- 2010/12/01 00:00 [pmc-release]
AID - 10.4258/hir.2010.16.4.253 [doi]
PST - ppublish
SO  - Healthc Inform Res. 2010 Dec;16(4):253-9. doi: 10.4258/hir.2010.16.4.253. Epub 
      2010 Dec 31.