PMID- 26151207
OWN - NLM
STAT- MEDLINE
DCOM- 20160208
LR  - 20230404
IS  - 1424-8220 (Electronic)
IS  - 1424-8220 (Linking)
VI  - 15
IP  - 7
DP  - 2015 Jul 3
TI  - H2RM: A Hybrid Rough Set Reasoning Model for Prediction and Management of 
      Diabetes Mellitus.
PG  - 15921-51
LID - 10.3390/s150715921 [doi]
AB  - Diabetes is a chronic disease characterized by high blood glucose level that 
      results either from a deficiency of insulin produced by the body, or the body's 
      resistance to the effects of insulin. Accurate and precise reasoning and 
      prediction models greatly help physicians to improve diagnosis, prognosis and 
      treatment procedures of different diseases. Though numerous models have been 
      proposed to solve issues of diagnosis and management of diabetes, they have the 
      following drawbacks: (1) restricted one type of diabetes; (2) lack 
      understandability and explanatory power of the techniques and decision; (3) 
      limited either to prediction purpose or management over the structured contents; 
      and (4) lack competence for dimensionality and vagueness of patient's data. To 
      overcome these issues, this paper proposes a novel hybrid rough set reasoning 
      model (H2RM) that resolves problems of inaccurate prediction and management of 
      type-1 diabetes mellitus (T1DM) and type-2 diabetes mellitus (T2DM). For 
      verification of the proposed model, experimental data from fifty patients, 
      acquired from a local hospital in semi-structured format, is used. First, the 
      data is transformed into structured format and then used for mining prediction 
      rules. Rough set theory (RST) based techniques and algorithms are used to mine 
      the prediction rules. During the online execution phase of the model, these rules 
      are used to predict T1DM and T2DM for new patients. Furthermore, the proposed 
      model assists physicians to manage diabetes using knowledge extracted from online 
      diabetes guidelines. Correlation-based trend analysis techniques are used to 
      manage diabetic observations. Experimental results demonstrate that the proposed 
      model outperforms the existing methods with 95.9% average and balanced 
      accuracies.
FAU - Ali, Rahman
AU  - Ali R
AD  - Department of Computer Engineering, Kyung Hee University, Seocheon-dong, 
      Giheung-gu Yongin-si, Gyeonggi-do 446-701, Korea. rahmanali@oslab.khu.ac.kr.
FAU - Hussain, Jamil
AU  - Hussain J
AD  - Department of Computer Engineering, Kyung Hee University, Seocheon-dong, 
      Giheung-gu Yongin-si, Gyeonggi-do 446-701, Korea. jamil@oslab.khu.ac.kr.
FAU - Siddiqi, Muhammad Hameed
AU  - Siddiqi MH
AD  - Department of Computer Engineering, Kyung Hee University, Seocheon-dong, 
      Giheung-gu Yongin-si, Gyeonggi-do 446-701, Korea. siddiqi@oslab.khu.ac.kr.
FAU - Hussain, Maqbool
AU  - Hussain M
AD  - Department of Computer Engineering, Kyung Hee University, Seocheon-dong, 
      Giheung-gu Yongin-si, Gyeonggi-do 446-701, Korea. 
      maqbool.hussain@oslab.khu.ac.kr.
FAU - Lee, Sungyoung
AU  - Lee S
AD  - Department of Computer Engineering, Kyung Hee University, Seocheon-dong, 
      Giheung-gu Yongin-si, Gyeonggi-do 446-701, Korea. sylee@oslab.khu.ac.kr.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20150703
PL  - Switzerland
TA  - Sensors (Basel)
JT  - Sensors (Basel, Switzerland)
JID - 101204366
SB  - IM
MH  - Artificial Intelligence
MH  - Diabetes Mellitus/*diagnosis/epidemiology/*therapy
MH  - *Electronic Health Records
MH  - Humans
MH  - *Medical Informatics
MH  - *Models, Statistical
MH  - Prognosis
PMC - PMC4541861
OTO - NOTNLM
OT  - H2RM
OT  - RBR
OT  - classification
OT  - diabetes mellitus
OT  - prediction
OT  - reasoning
OT  - regression
OT  - rough set theory
OT  - rules mining
OT  - trend analysis
EDAT- 2015/07/08 06:00
MHDA- 2016/02/09 06:00
PMCR- 2015/07/03
CRDT- 2015/07/08 06:00
PHST- 2015/05/07 00:00 [received]
PHST- 2015/06/20 00:00 [revised]
PHST- 2015/06/25 00:00 [accepted]
PHST- 2015/07/08 06:00 [entrez]
PHST- 2015/07/08 06:00 [pubmed]
PHST- 2016/02/09 06:00 [medline]
PHST- 2015/07/03 00:00 [pmc-release]
AID - s150715921 [pii]
AID - sensors-15-15921 [pii]
AID - 10.3390/s150715921 [doi]
PST - epublish
SO  - Sensors (Basel). 2015 Jul 3;15(7):15921-51. doi: 10.3390/s150715921.