[Analysis of the characteristics of the older adults with depression using data mining decision tree analysis].

PURPOSE: The purpose of this study was to develop a prediction model for the characteristics of older adults with depression using the decision tree method. METHODS: A large dataset from the 2008 Korean Elderly Survey was used and data of 14,970 elderly people were analyzed. Target variable was depression and 53 input variables were general characteristics, family & social relationship, economic status, health status, health behavior, functional status, leisure & social activity, quality of life, and living environment. Data were analyzed by decision tree analysis, a data mining technique using SPSS Window 19.0 and Clementine 12.0 programs. RESULTS: The decision trees were classified into five different rules to define the characteristics of older adults with depression. Classification & Regression Tree (C&RT) showed the best prediction with an accuracy of 80.81% among data mining models. Factors in the rules were life satisfaction, nutritional status, daily activity difficulty due to pain, functional limitation for basic or instrumental daily activities, number of chronic diseases and daily activity difficulty due to disease. CONCLUSION: The different rules classified by the decision tree model in this study should contribute as baseline data for discovering informative knowledge and developing interventions tailored to these individual characteristics.

Comorbidity study on type 2 diabetes mellitus using data mining.

BACKGROUND/AIMS: The aim of this study was to analyze comorbidity in patients with type 2 diabetes mellitus (T2DM) by using association rule mining (ARM). METHODS: We used data from patients who visited Keimyung University Dongsan Medical Center from 1996 to 2007. Of 411,414 total patients, T2DM was present in 20,314. The Dx Analyze Tool was developed for data cleansing and data mart construction, and to reveal associations of comorbidity. RESULTS: Eighteen associations reached threshold (support, ≥ 3%; confidence, ≥ 5%). The highest association was found between T2DM and essential hypertension (support, 17.43%; confidence, 34.86%). Six association rules were found among three comorbid diseases. Among them, essential hypertension was an important node between T2DM and stroke (support, 4.06%; confidence, 8.12%) as well as between T2DM and dyslipidemia (support, 3.44%; confidence, 6.88%). CONCLUSIONS: Essential hypertension plays an important role in the association between T2DM and its comorbid diseases. The Dx Analyze Tool is practical for comorbidity studies that have an enormous clinical database.

Diagnostic analysis of patients with essential hypertension using association rule mining.

OBJECTIVES: The purpose of this study was to analyze the records of patients diagnosed with essential hypertension using association rule mining (ARM). METHODS: Patients with essential hypertension (ICD code, I10) were extracted from a hospital's data warehouse and a data mart constructed for analysis. Apriori modeling of the ARM method and web node in the Clementine 12.0 program were used to analyze patient data. RESULTS: Patients diagnosed with essential hypertension totaled 5,022 and the diagnostic data extracted from those patients numbered 53,994. As a result of the web node, essential hypertension, non-insulin dependent diabetes mellitus (NIDDM), and cerebral infarction were shown to be associated. Based on the results of ARM, NIDDM (support, 35.15%; confidence, 100%) and cerebral infarction (support, 21.21%; confidence, 100%) were determined to be important diseases associated with essential hypertension. CONCLUSIONS: Essential hypertension was strongly associated with NIDDM and cerebral infarction. This study demonstrated the practicality of ARM in co-morbidity studies using a large clinic database.

[The effects of hope intervention on hope and depression of cancer patients staying at home].

PURPOSE: This study was to identify the effects of hope intervention on hope and depression of cancer patients staying at home. METHODS: The study design was a randomized control group design. The subjects consisted of forty cancer patients randomly selected who were registered at S-Gu Public Health Center. Hope intervention, which was composed of hope assessment, hope objective setting, positive self identity formation, therapeutic relationships, spiritual & transcendental process improvement, positive environmental formation and hope evaluation, was provided from November 20, 2006 to January 26, 2007. RESULTS: The 1-1 hypothesis, "The experimental group which received hope intervention will have a higher score of hope than the control group", was supported(t=-3.253, p= .003). The 1-2 hypothesis, "The experimental group which received hope intervention will have a higher level of hope index than the control group", was supported (t=-4.001, p= .000). Therefore the 1st hypothesis, "The experimental group which received hope intervention will have a higher level of hope than the control group" was supported. The 2nd hypothesis, "The experimental group which received hope intervention will have a lower level of depression than the control group", was not supported (t=1.872, p= .070). CONCLUSION: Hope intervention is an effective nursing intervention to enhance hope for patient with cancer.