• Title, Summary, Keyword: decision tree analysis

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A Comparative Study of Medical Data Classification Methods Based on Decision Tree and System Reconstruction Analysis

  • Tang, Tzung-I;Zheng, Gang;Huang, Yalou;Shu, Guangfu;Wang, Pengtao
    • Industrial Engineering and Management Systems
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    • v.4 no.1
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    • pp.102-108
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    • 2005
  • This paper studies medical data classification methods, comparing decision tree and system reconstruction analysis as applied to heart disease medical data mining. The data we study is collected from patients with coronary heart disease. It has 1,723 records of 71 attributes each. We use the system-reconstruction method to weight it. We use decision tree algorithms, such as induction of decision trees (ID3), classification and regression tree (C4.5), classification and regression tree (CART), Chi-square automatic interaction detector (CHAID), and exhausted CHAID. We use the results to compare the correction rate, leaf number, and tree depth of different decision-tree algorithms. According to the experiments, we know that weighted data can improve the correction rate of coronary heart disease data but has little effect on the tree depth and leaf number.

Two-Stage Decision Tree Analysis for Diagnosis of Personal Sasang Constitution Medicine Type (사상체질 판별을 위한 2단계 의사결정 나무 분석)

  • Jin, Hee-Jeong;Lee, Hae-Jung;Kim, Myoung-Geun;Kim, Hong-Gie;Kim, Jong-Yeol
    • Journal of Sasang Constitutional Medicine
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    • v.22 no.3
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    • pp.87-97
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    • 2010
  • 1. Objectives: In SCM, a personal Sasang constitution must be determined accurately before any Sasang treatment. The purpose of this study is to develop an objective method for classification of Sasang constitution. 2. Methods: We collected samples from 5 centers where SCM is practiced, and applied two-stage decision tree analysis on these samples. We recruited samples from 5 centers. The collected data were from subjects whose response to herbal medicine was confirmed according to Sasang constitution. 3. Results: The two-stage decision tree model shows higher classification power than a simple decision tree model. This study also suggests that gender must be considered in the first stage to improve the accuracy of classification. 4. Conclusions: We identified important factors for classifying Sasang constitutions through two-stage decision tree analysis. The two-stage decision tree model shows higher classification power than a simple decision tree model.

Decision Tree with Optimal Feature Selection for Bearing Fault Detection

  • Nguyen, Ngoc-Tu;Lee, Hong-Hee
    • Journal of Power Electronics
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    • v.8 no.1
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    • pp.101-107
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    • 2008
  • In this paper, the features extracted from vibration time signals are used to detect the bearing fault condition. The decision tree is applied to diagnose the bearing status, which has the benefits of being an expert system that is based on knowledge history and is simple to understand. This paper also suggests a genetic algorithm (GA) as a method to reduce the number of features. In order to show the potentials of this method in both aspects of accuracy and simplicity, the reduced-feature decision tree is compared with the non reduced-feature decision tree and the PCA-based decision tree.

A Decision Tree-based Analysis for Paralysis Disease Data

  • Shin, Yangkyu
    • Communications for Statistical Applications and Methods
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    • v.8 no.3
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    • pp.823-829
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    • 2001
  • Even though a rapid development of modem medical science, paralysis disease is a highly dangerous and murderous disease. Shin et al. (1978) constructed the diagnosis expert system which identify a type of the paralysis disease from symptoms of a paralysis disease patients by using the canonical discriminant analysis. The decision tree-based analysis, however, has advantages over the method used in Shin et al. (1998), such as it does not need assumptions - linearity and normality, and suggest appropriate diagnosis procedure which is easily explained. In this paper, we applied the decision tree to construct the model which Identify a type of the paralysis disease.

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Selection of the Optimal Decision Tree Model Using Grid Search Method : Focusing on the Analysis of the Factors Affecting Job Satisfaction of Workplace Reserve Force Commanders (격자탐색법을 이용한 의사결정나무 분석 최적 모형 선택 : 직장예비군 지휘관의 직장만족도에 대한 영향 요인 분석을 중심으로)

  • Jeong, Chulwoo;Jeong, Won Young;Shin, David
    • Journal of the Korean Operations Research and Management Science Society
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    • v.40 no.2
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    • pp.19-29
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    • 2015
  • The purpose of this study is to suggest the grid search method for selecting an optimal decision tree model. It chooses optimal values for the maximum depth of tree and the minimum number of observations that must exist in a node in order for a split to be attempted. Therefore, the grid search method guarantees building a decision tree model that shows more precise and stable classifying performance. Through empirical analysis using data of job satisfaction of workplace reserve force commanders, we show that the grid search method helps us generate an optimal decision tree model that gives us hints for the improvement direction of labor conditions of Korean workplace reserve force commanders.

A Development of Suicidal Ideation Prediction Model and Decision Rules for the Elderly: Decision Tree Approach (의사결정나무 기법을 이용한 노인들의 자살생각 예측모형 및 의사결정 규칙 개발)

  • Kim, Deok Hyun;Yoo, Dong Hee;Jeong, Dae Yul
    • The Journal of Information Systems
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    • v.28 no.3
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    • pp.249-276
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    • 2019
  • Purpose The purpose of this study is to develop a prediction model and decision rules for the elderly's suicidal ideation based on the Korean Welfare Panel survey data. By utilizing this data, we obtained many decision rules to predict the elderly's suicide ideation. Design/methodology/approach This study used classification analysis to derive decision rules to predict on the basis of decision tree technique. Weka 3.8 is used as the data mining tool in this study. The decision tree algorithm uses J48, also known as C4.5. In addition, 66.6% of the total data was divided into learning data and verification data. We considered all possible variables based on previous studies in predicting suicidal ideation of the elderly. Finally, 99 variables including the target variable were used. Classification analysis was performed by introducing sampling technique through backward elimination and data balancing. Findings As a result, there were significant differences between the data sets. The selected data sets have different, various decision tree and several rules. Based on the decision tree method, we derived the rules for suicide prevention. The decision tree derives not only the rules for the suicidal ideation of the depressed group, but also the rules for the suicidal ideation of the non-depressed group. In addition, in developing the predictive model, the problem of over-fitting due to the data imbalance phenomenon was directly identified through the application of data balancing. We could conclude that it is necessary to balance the data on the target variables in order to perform the correct classification analysis without over-fitting. In addition, although data balancing is applied, it is shown that performance is not inferior in prediction rate when compared with a biased prediction model.

Basic Tongue Diagnosis Indicators for Pattern Identification in Stroke Using a Decision Tree Method

  • Lee, Ju Ah;Lee, Jungsup;Ko, Mi Mi;Kang, Byoung-Kab;Lee, Myeong Soo
    • The Journal of Korean Medicine
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    • v.33 no.4
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    • pp.1-8
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    • 2012
  • Objectives: The purpose of this study was to specify major tongue diagnostic indicators and evaluate their significance in discriminating pattern identification subtypes in stroke patients. Methods: This study used a community based multi-center observational design. Participants (n=1,502) were stroke patients admitted to 11 oriental medical university hospitals between December 2006 and February 2010. To determine which tongue indicator affected each pattern identification, a decision tree analysis of the chi-square automatic interaction detector (CHAID) algorithm was performed. The chi-squared test was used as the criterion in splitting data with a p-value less than 0.05 for division, which is the main procedure for developing a decision tree. The minimum sample size for each node was specified as n =10, and branching was limited to two levels. Results: From the 9 tongue diagnostic indicators, 6 major tongue indicators (red tongue, pale tongue, yellow fur, white fur, thick fur, and teeth-marked tongue) were identified through the decision tree analysis. Furthermore, each pattern identification was composed of specific combinations of the 6 major tongue indicators. Conclusions: This study suggests that the 6 tongue indicators identified through the decision tree analysis can be used to discriminate pattern identification subtypes in stroke patients. However, it is still necessary to re-evaluate other pattern identification indicators to further the objectivity and reliability of traditional Korean medicine.

Waste Database Analysis Joined with Local Information Using Decision Tree Techniques

  • Park, Hee-Chang;Cho, Kwang-Hyun
    • 한국데이터정보과학회:학술대회논문집
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    • pp.164-173
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    • 2005
  • Data mining is the method to find useful information for large amounts of data in database. It is used to find hidden knowledge by massive data, unexpectedly pattern, relation to new rule. The methods of data mining are decision tree, association rules, clustering, neural network and so on. The decision tree approach is most useful in classification problems and to divide the search space into rectangular regions. Decision tree algorithms are used extensively for data mining in many domains such as retail target marketing, fraud detection, data reduction and variable screening, category merging, etc. We analyze waste database united with local information using decision tree techniques for environmental information. We can use these decision tree outputs for environmental preservation and improvement.

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A study of constitution diagnosis using decision tree method (의사결정나무법을 이용한 체질진단에 관한 연구)

  • Lee, Yong-Seop;Park, Seong-Sik;Park, Eun-Kyung
    • Journal of Sasang Constitutional Medicine
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    • v.13 no.2
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    • pp.144-155
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    • 2001
  • By the increasing concern about Sasang Constitution Medicine, its practical use is considered very important in disease prevention and medical treatment. However, the method of constitution classification is depending on the doctor's clinical trials because of the lack of the objective test criteria. This study is trying to improve the objectiveness of diagnosis using a new statistical method, decision tree. Decision tree method-a classification technique in the statistical analysis- was used to analyze the result of QSCCII instead of using discriminant analysis. As a result, 16 among 121 QSCCII questions was selected as important questions and 21 terminal nodes was built to classify the constitution. Using only 16 questions shown in the result of decision tree, we can diagnose and interpret the constitution easily and effectively.

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Analysis of the Characteristics of the Older Adults with Depression Using Data Mining Decision Tree Analysis (의사결정나무 분석법을 활용한 우울 노인의 특성 분석)

  • Park, Myonghwa;Choi, Sora;Shin, A Mi;Koo, Chul Hoi
    • Journal of Korean Academy of Nursing
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    • v.43 no.1
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    • pp.1-10
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    • 2013
  • 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.