隨著健康檢查的普遍化，醫院內健檢資料逐漸增加，對此龐大資料，如能正確地應用資料探勘手法進行分析，將可有效的幫助醫院提升醫療品質。然而，在醫療一般健檢上，存在著健檢者是否會回診之情況，其中包含許多原因，可能是醫療設施、品質或是服務上的問題。為了瞭解健檢者是否回診之主要因子，本研究藉由健檢資料以序列關聯(SPM)及關聯規則探勘方法預測健檢者回診之法則。首先，將一般健檢資料分成39組，並分析各組資料的顯著性。其次，對於每組資料用SPM產生頻繁項目集，針對頻繁項目集的變量利用關聯探勘找出健檢者是否會回診之重要法則，最後針對結果進行分析。本研究所找出之健檢者回診之關聯法則將可提供醫院對可能回診之健檢者進行相關之追蹤與行銷。

As the increasing of medical check-up popularity, there are a huge number of medical check-up data stored in database and have not been useful. These data actually can be very useful for future strategic planning if we mine it correctly. In other side, a lot of examinees come with unpredictable coming and also limited available facilities make medical check-up service offered by hospital not maximal. To solve that problem, this study used those medical check-up data to predict examinee re-coming. Sequential pattern mining (SPM) and association mining method were chosen because these methods are suitable for predicting examinee re-coming using sequential data. First, based on examinee personal information the data was grouped into 39 groups then discriminant analysis was done to check significant of the grouping. Second, for each group some frequent patterns were generated using SPM method. Third, based on frequent patterns of each group, pairs of variable can be extracted using association mining to get general pattern of re-coming examinee. Last, discussion and conclusion was done to give some implications of the results.

Agrawal, R., & Srikant, R. (1996). Mining sequential patterns: Generalizations and performance improvements. Springer Berlin Heidelberg.
Cheon, C., Oh, S.-M., Jang, S., Park, J.-S., Park, S., Jang, B.-H., et al. (2014). The Relationship between Health Behavior and General Health Status: Based on 2011 Korea National Health and Nutrition Examination Survey. Journal of Osong Public Health Res Perspect 5 (1) , 28-33.
Chiu, R.-k., & Yeh, Y.-C. (2010). Fuzzy-based dynamic scheduling system for health examination. International Conference on Machine Learning and Cybernetics (ICMLC) 2 , 636 - 641.
Cios, K. J., & Moore, G. W. (2002). Uniqueness of medical data mining. Journal of Artificial Intelligence in Medicine 26 (1-2) , 1-24.
Falkner, B., Gidding, S. S., Ramirez-Garnica, G., Wiltrout, S. A., West, D., & Rappaport, E. B. (2006). The relationship of body mass index and blood pressure in primary care pediatric patients. Journal of Pediatrics 148 (2) , 195-200.
Ford, E. S. (2004). The metabolic syndrome and mortality from cardiovascular disease and all-causes: findings from the National Health and Nutrition Examination Survey II Mortality Study. Journal of Atherosclerosis 173 (2) , 309–314.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2009). Multivariate Data Analysis (7th Edition). Prentice Hall.
Hu, X. (2003). DB-HReduction: A Data Preprocessing Algorithm for Data Mining Applications. Applied Mathematics Letter 16 (6) , 889-895.
Ji, Y., Ying, H., Dews, P., Mansour, A., Tran, J., Miller, R. E., et al. (2011). A Potential Causal Association Mining Algorithm for Screening Adverse Drug Reactions in Postmarketing Surveillance. IEEE Transactions on Information Technology in Biomedicine 15 (3) , 428 - 437.
Khaur, J., Lamb, M. M., & Ogden, C. L. (2015). The Association between Food Insecurity and Obesity in Children—The National Health and Nutrition Examination Survey. Journal of Academy of Nutrition and Dietetics 115 (5) , 751-758.
Klecka, W. R. (1980). Discriminant Analysis. California: SAGE Publications.
Koh, H. C., & Tan, G. (2005). Data mining applications in healthcare. Journal of Healthcare Information Management 19 (2) , 64–71.
Kontio, E., Airola, A., Pahikkala, T., Lundgren-Laine, H., Junttila, K., Korvenranta, H., et al. (2014). Predicting patient acuity from electronic patient records. Journal of Biomedical Informatics 51 (1) , 35-40.
Kotsiantis, S. B., Kanellopoulos, D., & Pintelas, P. E. (2006). Data Preprocessing for Supervised Leaning. International Journal of Computer Science 1 (2) , 111-117.
Lara, J. A., Lizcano, D., Pérez, A., & Valente, J. P. (2014). A general framework for time series data mining based on event analysis: Application to the medical domains of electroencephalography and stabilometry. Journal of Biomedical Informatics 51 (1) , 219-241.
Liao, S.-H., Chu, P.-H., & Hsiao, P.-Y. (2012). Data mining techniques and applications – A decade review from 2000 to 2011. Journal of Expert Systems with Applications 39 (12) , 11303-11311.
Ma, G., & Demeulemeester, E. (2013). A multilevel integrative approach to hospital case mix and capacity planning. Journal of Computers & Operations Research 40 (9) , 2198-2207.
McKeigue, P., Shah, B., & Marmot, M. (1991). Relation of central obesity and insulin resistance with high diabetes prevalence and cardiovascular risk in South Asians. The Lancet 337 (8738) , 382-386.
Mestre, A. M., Oliveira, M. D., & Barbosa-Póvoa, A. P. (2015). Location–allocation approaches for hospital network planning under uncertainty. European Journal of Operational Research 240 (3) , 791-806.
Mooney, C. H., & Roddick, J. F. (2013). Sequential pattern mining -- approaches and algorithms. Journal of ACM Computing Surveys 45 (2) , 1-39.
Munk, M., Kapusta, J., & Svee, P. (2012). Data Preprocessing Evaluation for Web Log Mining: Reconstruction of Activities of a Web Visitor. International Conference on Computational Science 1 (1) , 2273-2280.
Ohara, C., Inoue, K., Kashima, S., Inoue, M., & Akimoto, K. (2013). Undiagnosed diabetes has poorer profiles for cardiovascular and metabolic markers than known diabetes: The Yuport Medical Checkup Center Study. Journal of Diabetes Research and Clinical Practice 101 (2) , e7-e10.
Ou-Yang, C., Agustianty, S., & Wang, H.-C. (2013). Developing a data mining approach to investigate association between physician prescription and patient outcome – A study on re-hospitalization in Stevens–Johnson Syndrome. Journal of Computer Methods and Programs in Biomedicine 112 (1) , 84-91.
Perez-Iratxeta, C., Bork, P., & Andrade, M. A. (2002). Association of genes to genetically inherited diseases using data mining. Journal of Nature Genetics 31 (1) , 316 - 319.
Reckless, I. P., & Buchan, A. M. (2008). Stroke: management and prevention. Journal of Medicine 36 (11) , 592-600.
Resnick, H. E., Valsania, P., Halter, J. B., & Lin, X. (2000). Relation of weight gain and weight loss on subsequent diabetes risk in overweight adults. Journal of Epidemiol Community Health 54 (8) , 596-602.
Spitzer, W. 0. (1979). The periodic health examination. Canadian Task Force on the Periodic Health Examination. Canadian Medical Association Journal 121 (9) , 1193–1254.
Suka, M., Odajima, T., & Okamoto, M. (2014). Reading comprehension of health checkup reports and health literacy in Japanese people. Journal of Environmental Health and Preventive Medicine 19 (4) , 295-306.
Weinstein, A. R., Sesso, H. D., Lee, I. M., Cook, N. R., Manson, J. E., Buring, J. E., et al. (2004). Relationship of Physical Activity vs Body Mass Index With Type 2 Diabetes in Women. Journal of American Medical Association 292 (10) , 1188-1194.
Williams, E. I., & Fitton, F. (1988). Factors affecting early unplanned readmission of elderly patients to hospital. British Medical Journal 297 (1) , 784–787.