慢性疾病在全球各地因為可能導致死亡或者喪失常人應有的身體機能已經成為大家關注的議題。隨著工業發達、生活形態改變、人口數量成長、飲食習慣改變、汽車與工廠數量成長，伴隨環境污染的惡化使得慢性疾病患者人數逐年增加，台灣氣喘病患人數有逐漸增加的趨勢。財團法人兒童過敏氣喘及免疫學會[29]資料顯示台灣地區的兒童有11%罹患氣喘；而一項針對1974~2004年居住在台北地區之七到十五歲孩童統計顯示氣喘罹患人數已從1.3%增加至17.8%。[24]
本論文提出ㄧ套整合性之生理訊號探勘機制，能有效應用在慢性氣喘族群的生理訊號監測上，當使用者透過資訊網路平台登錄其生理訊號後，本系統可即時根據病人所登錄的氣喘相關生理資訊以及環境、氣象等資料，預測該病患氣喘發作風險的可能性。在對該病患的氣喘病狀作評估後，即時通知病患服藥的指示或保健訊息。經由真實資料之實驗分析，驗證本系統的準確率(precision)與涵蓋度(recall) 分別可達87.52% 及85.59%，實驗結果證明本系統能準確且有效的預測氣喘發作的可能。

Chronic diseases are the major causes of deaths and disabilities worldwide. The asthma population in Taiwan is also on the rise, thanks to rapid industrial development and changes in lifestyles and food consumption patterns. A report by Taiwan Children's Allergy, Asthma and Immune Institute shows that 11% of the children in Taiwan have asthma [29]. Furthermore, an analysis on asthmatic children in Taipei age seven to fifteen shows an increase of 16.5%, from 1.3% to 17.8%, between year 1974 and 2004 [24].

In our thesis, we proposed an integrated bio-signal data mining system, which monitors bio-signal for chronic asthma patients, and thus is able to calculate the probability of potential asthma attacks. This mechanism provides a user friendly platform that records a patient's daily bio-signal once the user logs in. Then, based on these bio-signal records, along with local air pollution levels and weather reports, our system predicts the chances of asthma attacks. Our studies show an 87.52% of precision and 85.59% of recall, which proves this mechanism to be effective and reliable in asthma attack prediction.

[1] R. Agrawal, R. Srikant, “Fast Algorithms for Mining Association Rules”, The 20th International Conference on Very Large Databases, Santiago, Chile, Sept. 1994.
[2] R. Agrawal, R. Srikant, “Mining Sequential Patterns,” Eleventh International Conference on Data Engineering 1995.
[3] M. Barua, H. Nazeran, P. Nava, B. Diong, M. Goldman, “Classification of Impulse Oscillometric Patterns of Lung Function in Asthmatic Children using Artificial Neural Networks,” Engineering in Medicine and Biology Society, 2005
[4] J. Choi, W. Y. Kang, J. Chung, J. W. Park, H. S. Lee and B. G. Min, “Development of an online database system for remote monitoring of artificial heart patient,” International IEEE EMBS Special Topic Conference on Information Technology Applications in Biomedicine, pp. 59- 61, 2003.
[5] H. T. Chu, C. C. Huang, Z. H. Lian, J.J.P. Tsai, “A ubiquitous warning system for asthma-inducement,” IEEE Sensor Networks, Ubiquitous, and Trustworthy Computing, 2006.
[6] O. Cure, “Designing Patient-Oriented Systems with Semantic Web Technologies,” IEEE Symposium on Computer-Based Medical Systems (CBMS), p. 195, 2003.
[7] J. Finkelstein, M.R. Cabrera, G. Hripcsak, “Web-based monitoring of asthma severity: a new approach toambulatory management,” Information Technology Applications in Biomedicine, 1998.
[8] E. Frank, M. A. Hall, Geoffrey Holmes, Richard Kirkby, Bernhard Pfahringer, Ian H, “Weka - a machine learning workbench for data mining,” The Data Mining and Knowledge Discovery Handbook, pages 1305-1314. Springer, 2005.
[9] R. L. Jan, J. Y. Wang, M. C. Huang, S. M. Tseng, H. J. Su, L.F. Liu. “An Internet-based interactive telemonitoring system for improving childhood asthma outcomes in Taiwan.” Telemedicine and e-Health, pp. 1-28, 2006.
[10] B. Liu, W. Hsu, Y. Ma , “Integrating Classification and Association Rule Mining,“ The Fourth International Conference on Knowledge Discovery and Data Mining, New York, USA, 1998.
[11] B. Liu, Y. Ma, C. K. Wong, and P. S. Yu. "Scoring the Data Using Association Rules." Applied Intelligence, Vol 18, No. 2, 119-135, 2003.
[12] O'Leary, M. Koolpiruck, D. Balachandran, W. Emberlin, J. Lewis, R. “The role of electrostatic charge accumulated by respirable sized allergens with regard to thunderstorm asthma,” Industry Applications Conference, 2005.
[13] J. S. Park et al., "An effective hash-based algorithm for mining association rules," ACM SIGMOD International Conference. Management of Data, 1995.
[14] J.R. Quinlan, “Induction Of Decision Trees. Machine Learning 1”, Kluwer Academic Press, 81-106, 1986.
[15] J. R. Quinlan. “C4.5 : programs for machine learning.” The Morgan Kaufmann series in machine learning. Morgan Kaufmann Publishers, San Mateo, Calif., 1993.
[16] J. R. Quinlan. “Induction of Decision Trees,” Machine Learning, Kluwer Academic Publishers, Vol. 1, Issue 1, pp. 81–106, 2003.
[17] J.R. Quinlan, “Improved Use of Continuous Attributes in C4.5”, Journal of Artificial Intelligence Research, 4 pages 77-90, 1996.
[18] A. Rajagiri, B. Diong, M. Goldman, H. Nazeran, “Can Asthma in Children be Detected by the Estimated Parameter Values of the Augmented RIC Model?” Engineering in Medicine and Biology Society, 2006.
[19] S. Ruggieri, “Efficient C4.5”, IEEE Transactions on Knowledge and Data Engineering, Vol.14 N.2, pp.438-444, 2002.
[20] C. L. Stokes, A. K. Lewis, T. Paterson, C. C. Leong, N. Defranoux, G. Bub Entelos, ” Asthma Physiolab: A Dynamic, Computer-Based Mathematical Model of Acute and Chronic Asthma,” The First Joint BMES/EMBS Conference Serving Humanity, Advancing Technology, Akmta, GA, USA, 1999.
[21] P. Várady, Z. Benyó, and B. Benyó, “An Open Architecture Patient Monitoring System Using Standard Technologies,” IEEE Transactions on Information Technology in Biomedicine, Vol. 6, No. 1, 2002.
[22] A. Veloso, W. Meira Jr., M. J. Zaki, “Lazy Associative Classification,”The Sixth International Conference on Data Mining, pp. 645-654, 2006.
[23] I. H. Witten, E. Frank, “Data Mining: Practical Machine Learning Tools and Techniques (Second Edition)” Morgan Kaufmann June 2005.
[24] D.C. Yan, L. S. Ou, T. L. Tsai, W. F. Wu, J. L. Huang, “Prevalence and severity of symptoms of asthma, rhinitis, and eczema in 13- to 14-year-old children in Taipei, Taiwan.” Ann Allergy Asthma Immune 95:579–585 2005.
[25] CBA-DMII [http://www.comp.nus.edu.sg/~dm2/index.html], Developed by National University of Singapore, 2005.
[26] 南瀛氣喘過敏兒保健協會[http://140.116.58.191/asthma/index.php], R.O.C., 2005.
[27] 行政院環保署環境資料庫[http://edb.epa.gov.tw/], R.O.C., 2005.
[28] 中央氣象局全球資訊網[http://www.cwb.gov.tw/], R.O.C., 2005.
[29] 財團法人兒童過敏及氣喘病學術文教基金會[http://www.asthma.org.tw/], R.O.C, 2006.