Birliktelik Kuralları Madenciliği İle Yatan Hasta Profilinin Çıkarılması

Year 2019, Cilt: 23 Özel Sayı, 1917 - 1926, 31.12.2019

Mehmet Ali Alan , Cavit Yeşilyurt

Abstract

Bu çalışmada, hastane veri tabanındaki veriler kullanılarak, veri madenciliği yöntemi ile hasta profili çıkarılmaya çalışılmıştır. Bu amaçla 28.738 yatan hastaya ait veriler kullanılarak Birliktelik Kuralları Madenciliği yapılmıştır. Yapılan çalışma sonucunda %60 ve üzeri güven seviyesinde 64 kural üretilebilmiştir. Üretilen bu kuralların hem doktorlara, hem de hastane yöneticilerine karar desteği sağlayacağı düşünülmektedir.

Keywords

Veri Madenciliği, Birliktelik Kuralları

References

• Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules. In Proceedings of the 20th international conference on very large data bases, 1994 Santiago, Chile. Citeseer (pp. 487–499).
• Arora, Jyoti; Bhalla, Nidhi and Rao,Sanjeev (2013), ‘A Review On Association Rule Mining Algorithms’ International Journal of Innovative Research in Computer and Communication Engineering 1.5: 1246-1251.
• Bose, I., Chun, L. A.,Yue, L. V. W., Ines, L. H. W. and Helen, W. O. L. (2009), ‘Business Data Warehouse: The Case of Wal-Mart’, Data Mining Applications for Empowering Knowledge Societies, Ed. Hakikur Rahman, Information Science Reference, pp.189-198
• Çoban, Ö., Karabey, I., Günay, F.B (2015), Acil Servis Verilerinden Birliktelik Kuralı (Apriori) Yöntemi ile Hasta Profilinin Çıkarılması, 2. Ulusal Yönetim Bilişim Sistemleri Kongresi, Erzurum, pp.275-282.
• Erdem, Sabri, Özdağoğlu, Güzin (2008), Ege Bölgesi’ndeki Bir Araştırma Ve Uygulama Hastanesinin Acil Hasta Verilerinin Veri Madenciliği İle Analiz Edilmesi, Anadolu Üniversitesi Bilim Ve Teknoloji Dergisi Anadolu Universıty Journal Of Scıence And Technology Cilt/Vol.:9-Sayı/No: 2 : 261-270 (2008)
• Fayyad, Usama, Gregory Piatetsky-Shapiro, and Padhraic Smyth (1996), ‘From data mining to knowledge discovery in databases’ AI magazine 17.3 pp. 37-54.
• Giudici, Paolo and Figini, Silvia (2009), Applied Data Mining For Business and Industry,Second Edition, Wiley Publicition, West Sussex.
• Han, Jiawei, and Micheline Kamber (2006). Data Mining, Southeast Asia Edition: Concepts and Techniques. Morgan Kaufmann.
• Isken, Mark W. and Rajagopalan, Balaji (2002), Data Mining to Support Simulation Modeling of Patient Flow in Hospitals, Journal of Medical Systems, Vol. 26, No. 2, April 2002, :179-197
• Jain, Yogendra Kumar, Vinod Kumar Yadav, and Geetika S. Panday (2011), ‘An efficient association rule hiding algorithm for privacy preserving data mining’ International Journal on Computer Science and Engineering 3.7, pp. 2792-2798.
• Kantardzic, Mehmed (2003), Data Mining: Concepts, Models, Methods, and Algorithms, John Wiley & Sons J. B. Speed Scientific School, University of Louisville IEEE Computer Society, Sponser.
• Krishnaiah,V., Narsimha, G. & Chandra, N. S. (2013), ‘A Study On Clinical Prediction Using Data Mining Techniques’, International Journal of Computer Science Engineering and Information Technology Research (IJCSEITR) ISSN 2249-6831 Vol. 3, Issue 1:239-248,
• Maimon, Oded, and Lior Rokach (2008), Data mining with decision trees: theory and applications, World Scientific New Jersey.
• Nahar J, Imam T, Tickle KS, Chen YP (2013) ‘Association Rule Mining To Detect Factors Which Contribute To Heart Disease in Males And Females’, Expert Systems with Applications 40.
• Nisbet, Robert, John Elder IV, and Gary Miner, (2009), Handbook of statistical analysis and data mining applications. Elsevier Inc, Burlington.
• Taş, Yusuf (2018), Birliktelik Kuralları Madenciliği Ve Bir Uygulama, Cumhuriyet Üniversitesi, Sosyal Bilimler Enstitüsü, Yüksek Lisans Tezi, Sivas
• Tsumoto, Shusaku, Hirano, Shoji, Tsumoto, Yuko (2011), Information Reuse in Hospital Information Systems: A Data Mining Approach, IEEE IRI 2011, August 3-5, 2011, Las Vegas, Nevada,:172-176
• Webb, G.,I. (2003). Association Rules. In Nong Ye (Edt.), The Handbook Of data Mining (pp. 27-28). New Jersey: Lawrence Erlbaum Associates,Inc.
• Wu, Tong and Li, Xiangyang (2003), ‘Data Storage and Management’, The Handbook of Data Mining, Ed. Nong Ye, New Jersey: Lawrence Erlbaum Associates, Inc. pp.393-407.