Volume 2. Lecture Notes in Electrical Engineering 215. Kim Chung Eds. IT Convergence and Security IT Convergence and Security 2012.

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1 Lecture Notes in Electrical Engineering 215 Kuinam J. Kim Kyung-Yong Chung Editors IT Convergence and Security 2012 Volume 2 The proceedings approaches the subject matter with problems in technical convergence and convergences of security technology. This approach is new because we look at new issues that arise from techniques converging. The general scope of the proceedings content is convergence security and the latest information technology. The intended readership are societies, enterprises, and research institutes, and intended content level is mid- to highly educated personals. The most important features and benefits of the proceedings are the introduction of the most recent information technology and its related ideas, applications and problems related to technology convergence, and its case studies and finally an introduction of converging existing security techniques through convergence security. Overall, through the proceedings, authors will be able to understand the most state of the art information strategies and technologies of convergence security. Kim Chung Eds. IT Convergence and Security 2012 Lecture Notes in Electrical Engineering 215 Kuinam J. Kim Kyung-Yong Chung Editors IT Convergence and Security 2012 Volume 2 Electrical Engineering ISBN

2 xvi Contents Efficient Isomorphic Decision for Mining Sub Graphs with a Cyclic Form Gangin Lee and Unil Yun Performance Evaluation of Approximate Pattern Mining Based on Probabilistic and Statistical Techniques Unil Yun, Gwangbum Pyun and Sung-Jin Kim Interactive Self-Diagnostic System Using Anatomical 3D Human Body S.-H. Kim and K.-Y. Chung Rule-Based Naive Bayesian Filtering for Personalized Recommend Service Jong-Hun Kim and Kyung-Yong Chung Design of an Actigraphy Based Architecture for Mental Health Evaluation Mi-hwa Song, Jae-Sung Noh, Seung-Min Yoo and Young-Ho Lee Efficient Detection of Content Polluters in Social Networks Jin Seop Han and Byung Joon Park A Prototype Selection Algorithm Using Fuzzy k-important Nearest Neighbor Method Zhen-Xing Zhang, Xue-Wei Tian, Sang-Hong Lee and Joon S. Lim Enhanced Reinforcement Learning by Recursive Updating of Q-values for Reward Propagation Yunsick Sung, Eunyoung Ahn and Kyungeun Cho Improved Method for Action Modeling Using Bayesian Probability Theory Yunsick Sung, Kyhyun Um and Kyungeun Cho Decision Tree Driven Rule Induction for Heart Disease Prediction Model: Korean National Health and Nutrition Examinations Survey V Jae-Kwon Kim, Eun-Ji Son, Young-Ho Lee and Dong-Kyun Park Data Mining-Driven Chronic Heart Disease for Clinical Decision Support System Architecture in Korea Eun-Ji Son, Jae-Kwon Kim, Young-Ho Lee and Eun-Young Jung

3 Decision Tree Driven Rule Induction for Heart Disease Prediction Model: Korean National Health and Nutrition Examinations Survey V-1 Jae-Kwon Kim, Eun-Ji Son, Young-Ho Lee and Dong-Kyun Park Abstract Heart disease has the highest rates of death in non-communicable disease and there have been much research on heart disease. Even though there is recognition for importance of heart disease prediction, related studies are insufficient. Therefore, to develop heart disease prediction model for Korean, we suggest data mining driven rule induction for heart disease prediction in this paper. Proposed method suggest heart disease prediction model by applying decision tree driven rule induction based on data set from Korean National Health and Nutrition Examinations Survey V-1 (KNHANES V-1). The prediction model is expected contribute to Korea s heart disease prediction. Keywords Data mining Heart disease prediction Decision tree Rule induction KNHANES V-1 J.-K. Kim School of Computer Science and Information Engineering, Inha University, 253, Yonghyun-dong, Nam-gu, Incheon, South Korea jaekwonkorea@naver.com E.-J. Son Y.-H. Lee School of Information Technology, Gachon University, 191, Hambakmoero, Yeonsu-gu, Incheon, South Korea a_projanga@naver.com Y.-H. Lee lyh@gachon.ac.kr D.-K. Park (&) u-healthcare Center, Gachon University Gil Hospital, 1198, Guwold-dong, Namdong-gu, Incheon, South Korea pdk66@gilhospital.com K. J. Kim and K.-Y. Chung (eds.), IT Convergence and Security 2012, Lecture Notes in Electrical Engineering 215, DOI: / _123, Ó Springer Science+Business Media Dordrecht

4 1016 J.-K. Kim et al. 1 Introduction Heart disease is continually a major cause for death in spite of costly and steady management. Prediction of heart disease can reduce health care costs and required for future national health promotion [1]. The most typical guideline predicting heart risk factor is Framingham Risk Score [2]. However, this guideline is not suitable for Korean. There are aware of the importance for the prediction of heart disease in Korea but the studies are lacking. Therefore, development of heart disease prediction model for Korean is necessary [3]. Data mining explores hidden rule or pattern and is analytical method for discovering new knowledge. Also heart disease prediction model using artificial neural network, fuzzy method and so on is showing high accuracy [4]. In this paper, we suggest data mining driven rule induction model for heart disease prediction of Korean. Proposed method suggest Korean s heart disease prediction model using decision tree driven rule induction. Decision tree is an analytical method of classification and prediction which is frequently used for clinical disease prediction [5]. In this paper, to develop heart disease prediction model for Korean, we used 5th Korean National Health and Nutrition Examinations Survey (KNHANES V-1) [6], and suggest prediction model using decision tree method, C Related Works Data mining is widely used for clinical medical data analysis and there are many studies in progress to predict heart disease. Vahid Khatibi [4] generated guideline of FRS and PROCAM, suggesting rulebase using Dempster-Shafer evidence theory, and suggests heart disease prediction model using fuzzy method. Sanz [4] predicted heart diseases using genetic algorithm and fuzzy method which referred to guideline of FRS. Erstwhile prediction model is not suitable for Korean because the model refers to FRS guideline. Therefore, for Korean s heart disease prediction, mining model based on Korean clinical data is required. 3 Rule Induction 3.1 Data Set In this paper, we used 5th Korean National Health and Nutrition Examinations Survey (KNHANES V-1, 2010) from Korea Centers for Disease Control and Prevention. KNHANES V-1 researched people s health and nutrition levels by National Organization statistics and provided a basis for policy formulation and

5 Decision Tree Driven Rule Induction for Heart Disease Prediction Model 1017 Table 1 General characteristics Attributes Descriptions Types Values Sex Sex Flag 1 = male; 2 = female Age Age Range [20,82] marri_1 Married Flag 1 = married; 2 = Single DE1_lt Diabetes status Flag 1 = Diabetes; 2 = absent BS3_1 Smoking status Set 0 = not Smoke; 1 = Smoking; 2 = Sometime; 3 = Past HE_sbp_tr Systolic blood Range [89,178] pressure HE_dbp_tr Diastolic blood Range [56,117] pressure HE_ht Height Range [142,186] HE_wt Weight Range [42,130] HE_chol Total Cholesterol Range [109,356] HE_HDL HDL cholesterol Range [28,102] HE_LDL_drct LDL cholesterol Range [39,243] D11_pr; D12_pr; D13_pr; D15_pr; D16_pr CHD diagnosis (Output) Flag 0 = absent; 1 = present evaluation such as National Health Promotion Plan [6]. Among 8019 total respondents, 6,336 uncertain respondents, 23 of subjects under 20 years of age, 1,228 respondents who did not respond heart disease related survey were excluded. Finally, 432 cases were selected, input variables were total 12, and output variables were total 5. Output variable was the prevalence of hypertension, hyperlipidemia, stroke, myocardial infarction and angina. If there were more than one disease prevalence, we defined as heart disease (Table 1). 3.2 Decision Tree Based Rule Induction Decision tree is one of classification method consist of root node, internal node, leaf of terminal node and expressed by type of If A, then B, Else C. C 5.0 is an inductive learning method based on C 4.5 categorized by Entropy. C 5.0 is useful to conduct pruning if decision tree have too many levels and leaf node which improves generalization ability of learning decision tree. Analysis procedures of decision tree follow four steps. Step (1) Decision tree creation: Shape a decision tree after considering appropriate separation standards and stopping rule, in accordance with the purpose of analysis and the structure of data. Step (2) Pruning: Remove branches having improper inference rules or risk of classification error.

6 1018 J.-K. Kim et al. Step (3) Step (4) Feasibility Evaluation: Evaluate decision tree through risk chart, profit chart, or cross-feasibility evaluation. Model Analysis and Prediction: Interpret the decision tree model result and set up the prediction model. In this paper, prediction model for Korean s heart disease were created using rule induction of C 5.0. C 5.0 is easily interpreted and analyzed by propositional knowledge creating If Then Rule. Therefore, the rule was created by applying rule induction algorithm to KNHNES-V1). 4 Experiment and Result The scenario of the experiment is shown in Fig. 1. Data of 432 people for experiment consist of training data 216(50 %), testing data 216(50 %). Also, C 5.0 induction rule algorithm was applied using Clementine 11.1(SPSS, INC., Chicago, USA). C 5.0 induction rule algorithm option was set-up as Pruning Severity = 75 %, Minimum records per child branch = 2. To reduce error rate of rule, boosting was designed for ten circuits. Figure 2 shows generated rule through training data. The rule predicting heart disease was total 15, each of the rules indicates status if disease prevalence. The rule evaluated accuracy; data set of proposed model and FRS guideline were compared [2]. The evaluation results are shown in Table 2. FRS had low accuracy because it is not based on Korean s heart disease prediction. On the other hand, proposed model had high accuracy because it used Korean data set. Therefore, proposed model can be a significant model on Korean s heart disease prediction. Fig. 1 Experiment scenario

7 Decision Tree Driven Rule Induction for Heart Disease Prediction Model 1019 Fig. 2 The result of the generate decision tree detailed rules Table 2 Experiment result FRS guideline Propose (testing set) Male Female Male Female Accuracy (%) Conclusion Heart disease prediction is very important, and requires prediction guideline for Korean. In this paper, we suggested data mining driven heart disease prediction. Proposed model used data set of Korean National Health and Nutrition Examinations Survey and decision tree driven C 5.0 rule induction algorithm. As the experimental results, proposed model had higher accuracy than abroad guideline. Proposed prediction model is expected to contribute to Korea heart disease prediction. Acknowledgments This work was supported by the R&D Program of MKE/KEIT [ , Development of Digital TV based u-health System using AI]. References 1. World Health Organization (2010) The world health report /whr08_en.pdf. Accessed Nov Wilson P, D Agostino R, Levy D, Belanger A, Silbershatz H, Kannel W (1998) Prediction of coronary heart disease using risk factor categories. Circulation 97:

8 1020 J.-K. Kim et al. 3. Do Young L, Eun Jung R, Eun Suk C, Ji Hoon K, Jong Chul W, Cheol Young P, Won Young L, Ki Won O, Sung Woo P, Sun Woo K (2008) Comparison of the predictability of cardiovascular disease risk according to different metabolic syndrome criteria of American heart association/national heart, lung, and blood institute and international diabetes federation in Korean men. J Diabetes Metab 32(4): Vahid K, Gholam AM (2010) A fuzzy-evidential hybrid inference engine for coronary heart disease risk assessment. Int J Expert Syst Appl 37(12): Anooj PK (2012) Clinical decision support system: risk level prediction of heart disease using decision tree fuzzy rules. Int J IJRRCS 3(3): Korea Centers for Disease Control and Prevention (2010) 5th Korean national health and nutrition examinations survey (KNHANES V-1). Centers for Disease Control and Prevention, Seoul 7. Pagola M, Bustince H, Brugos A, Fernandez A, Herrera F (2011) A case study on medical diagnosis of cardiovascular diseases using a genetic algorithm for tuning fuzzy rule-based classification systems with interval-valued fuzzy sets. In: Proceedings of 2011 IEEE symposium on advances in T2FUZZ, pp 9 15