Feature selection and classification model construction on type 2 diabetic patients' data

Artificial Intelligence in Medicine

Volumn 41, Issue 3, 2007, Pages 251-262

  Huang, Yue ^a   McCullagh, Paul ^b   Black, Norman ^b   Harper, Roy ^c  

^a IMPERIAL COLLEGE LONDON   (United Kingdom)

^b UNIVERSITY OF ULSTER   (United Kingdom)

^c ULSTER HOSPITAL   (United Kingdom)

Author keywords

Blood glucose; Classification; Data mining; Feature selection; Type 2 diabetes

Indexed keywords

COMPUTATIONAL METHODS; DATA MINING; DISEASE CONTROL; FEATURE EXTRACTION; OPTIMIZATION; PATIENT TREATMENT;

BLOOD GLUCOSE; CLASSIFICATION; HEALTH ECONOMICS; TYPE 2 DIABETES;

MEDICAL COMPUTING;

GLUCOSE;

ADULT; AGED; ARTICLE; CONTROLLED STUDY; DATA MINING; DIABETES CONTROL; DISEASE CLASSIFICATION; GLUCOSE BLOOD LEVEL; HUMAN; NON INSULIN DEPENDENT DIABETES MELLITUS; OUTCOME ASSESSMENT; PATIENT CARE; PRIORITY JOURNAL;

ADMINISTRATION, ORAL; ADULT; AGED; AGED, 80 AND OVER; ALGORITHMS; BLOOD GLUCOSE; BODY MASS INDEX; DECISION SUPPORT SYSTEMS, CLINICAL; DIABETES MELLITUS, TYPE 2; FEMALE; HEALTH KNOWLEDGE, ATTITUDES, PRACTICE; HUMANS; HYPOGLYCEMIC AGENTS; INFORMATION STORAGE AND RETRIEVAL; INJECTIONS; LIFE STYLE; MALE; MEDICAL RECORDS SYSTEMS, COMPUTERIZED; MIDDLE AGED; MODELS, BIOLOGICAL; OBESITY; PATIENT EDUCATION AS TOPIC; PATIENT SELECTION; REPRODUCIBILITY OF RESULTS; RISK FACTORS; SMOKING; TREATMENT OUTCOME;

EID: 35548975678     PISSN: 09333657     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.artmed.2007.07.002     Document Type: Article

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