Knowledge Discovery in a Community Data Set: Malnutrition among the Elderly

Objectives The purpose of this study was to design a prediction model that explains the characteristics of elderly adults at risk of malnutrition. Methods Data were obtained from a large data set, 2008 Korean Elderly Survey, in which the data of 15,146 subjects were entered. With nutritional status a target variable, the input variables included the demographic and socioeconomic status of participants. The data were analyzed by using the SPSS Clementine 12.0 program's feature selection node to select meaningful variables. Results Among the C5.0, C&R Tree, QUEST, and CHAID models, the highest predictability was reported by C&R Tree with the accuracy rate of 77.1%. The presence of more than two comorbidities, living alone status, having severe difficulty in daily activities, and lower perceived economic status were identified as risk factors of malnutrition in elderly. Conclusions A reliable decision support model was designed to provide accurate information regarding the characteristics of elderly individuals with malnutrition. The findings demonstrated the good feasibility of data mining when used for a large community data set and its value in assisting health professionals and local decision makers to come up with effective strategies for achieving public health goals.

[1]  J. Hallick,et al.  Analytics and the data warehouse. , 2001, Health management technology.

[2]  Sung-il Cho,et al.  Employment status and depressive symptoms in Koreans: results from a baseline survey of the Korean Longitudinal Study of Aging. , 2009, The journals of gerontology. Series B, Psychological sciences and social sciences.

[3]  Francisco Herrera,et al.  Enhancing the effectiveness and interpretability of decision tree and rule induction classifiers with evolutionary training set selection over imbalanced problems , 2009, Appl. Soft Comput..

[4]  Marion J. Ball,et al.  Nursing Informatics: Where Caring and Technology Meet , 1995 .

[5]  Koh Hc,et al.  Data mining applications in the context of casemix. , 2001 .

[6]  Myonghwa Park,et al.  [Analysis of the characteristics of the older adults with depression using data mining decision tree analysis]. , 2013, Journal of Korean Academy of Nursing.

[7]  A Sheiham,et al.  Does the condition of the mouth and teeth affect the ability to eat certain foods, nutrient and dietary intake and nutritional status amongst older people? , 2001, Public Health Nutrition.

[8]  Peter C Austin,et al.  Logistic regression had superior performance compared with regression trees for predicting in-hospital mortality in patients hospitalized with heart failure. , 2010, Journal of clinical epidemiology.

[9]  Jane V. White,et al.  Assessing Health Status in the Elderly: The Nutrition Screening Initiative , 2010, Journal of health care for the poor and underserved.

[10]  Hallick Jn,et al.  Analytics and the data warehouse. , 2001 .

[11]  S. Cropper.  Collaborative Working and the Issue of Sustainability , 1996 .

[12]  Gregory Piatetsky-Shapiro,et al.  Advances in Knowledge Discovery and Data Mining , 2004, Lecture Notes in Computer Science.

[13]  J. Jones,et al.  The methodology of nutritional screening and assessment tools. , 2002, Journal of human nutrition and dietetics : the official journal of the British Dietetic Association.

[14]  J. Fitzpatrick.  Oral health care needs of dependent older people: responsibilities of nurses and care staff. , 2000, Journal of advanced nursing.

[15]  Patricia A. Abbott.  Knowledge Discovery in Large Data Sets: A Primer for Data Mining Applications in Health Care , 2000 .

[16]  E. Clays,et al.  Malnutrition and associated factors in elderly hospital patients: a Belgian cross-sectional, multi-centre study. , 2010, Clinical nutrition.

[17]  H. Koh,et al.  Data mining applications in the context of casemix. , 2001, Annals of the Academy of Medicine, Singapore.

[18]  J Studnicki,et al.  Comparing alternative methods for composing community peer groups: a data warehouse application. , 2001, Journal of public health management and practice : JPHMP.

[19]  Victor R. Preedy,et al.  Korea Ministry of Health and Welfare , 2010 .

[20]  Jiuyong Li,et al.  Efficient discovery of risk patterns in medical data , 2009, Artif. Intell. Medicine.

[21]  C. Huxham.  Creating Collaborative Advantage , 1997 .