Abstract
The large amounts of data generated by healthcare transactions are too complex and voluminous to be processed and analysed by traditional methods. Data mining can improve decision-making by discovering patterns and trends in large amounts of complex data. In the healthcare industry specifically, data mining can be used to decrease costs by increasing efficiency, improve patient quality of life, and perhaps most importantly, save the lives of more patients. The main goal of this project is to apply data mining techniques in order to make possible the prediction of the degree of disability that patients will present when they leave hospitalization. The clinical data that will compose the data set was obtained from one single hospital and contains information about patients who were hospitalized in Cardio Vascular Disease’s (CVD) unit in 2016 for having suffered a cardiovascular accident. To develop this project, it will be used the Waikato Environment for Knowledge Analysis (WEKA) machine learning Workbench since this one allows users to quickly try out and compare different machine learning methods on new data sets
References
[1] USF Health, Data Mining In Healthcare. https://www.usfealthonline.com/re-sources/healthcare/data-mining-in-healthcare/ (Online). (Accessed in: 26-05-2017).Search in Google Scholar
[2] Koh, H. C., & Tan, G. (2011). Data mining applications in healthcare. Journal of healthcare information management, 19(2), 65. Search in Google Scholar
[3] Jothi, N., & Husain, W. (2015). Data mining in healthcare - a review. Procedia Computer Science, 72, 306-313.10.1016/j.procs.2015.12.145Search in Google Scholar
[4] Vlahos, G. E., Ferratt, T. W., & Knoepfle, G. (2004). The use of computer-based information systems by German managers to support decision making. Information & Management, 41(6), 763-779.10.1016/j.im.2003.06.003Search in Google Scholar
[5] 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), 1(3), 239-248.Search in Google Scholar
[6] Durairaj, M., & Ranjani, V. (2013). Data mining applications in healthcare sector: a study. Int. J. Sci. Technol. Res. IJSTR, 2(10).Search in Google Scholar
[7] Kharya, S. (2012). Using data mining techniques for diagnosis and prognosis of cancer disease. arXiv preprint arXiv:1205.192310.5121/ijcseit.2012.2206Search in Google Scholar
[8] Kohavi, R., & Quinlan, J. R. (2002, January). Data mining tasks and methods: Classification: decision-tree discovery. In Handbook of data mining and knowledge discovery (pp. 267-276). Oxford University Press, Inc.Search in Google Scholar
[9] Wide Skills, Data Mining Tasks. http://www.wideskills.com/data-mining-tutorial/05-data-mining-tasks (Online). (Accessed in: 28-05-2017).Search in Google Scholar
[10] Yoo, I., Alafaireet, P., Marinov, M., Pena-Hernandez, K., Gopidi, R., Chang, J. F., & Hua, L. (2012). Data mining in healthcare and biomedicine: a survey of the literature. Journal of medical systems, 36(4), 2431-2448.10.1007/s10916-011-9710-5Search in Google Scholar PubMed
[11] Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. AImagazine, 17(3), 37.Search in Google Scholar
[12] Goebel, M., & Gruenwald, L. (1999). A survey of data mining and knowledge discovery software tools. ACM SIGKDD explorations newsletter, 1(1), 20-33.10.1145/846170.846172Search in Google Scholar
[13] T, ĂRANU, I. (2016). Data mining in healthcare: decision making and precision. Database Systems Journal BOARD, 33.Search in Google Scholar
[14] Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). The KDD process for extracting useful knowledge from volumes of data. Communications of the ACM, 39(11), 27-34.10.1145/240455.240464Search in Google Scholar
[15] Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009). The WEKA data mining software: an update. ACM SIGKDD explorations newsletter, 11(1), 10-18.10.1145/1656274.1656278Search in Google Scholar
[16] Frank, E., Hall, M., Trigg, L., Holmes, G., & Witten, I. H. (2004). Data mining in bioinformatics using Weka. Bioinformatics, 20(15), 2479-2481.10.1093/bioinformatics/bth261Search in Google Scholar PubMed
[17] pentaho, A Hitachi Group Company, Data Mining - Weka. http://community.pen-taho.com/projects/data-mining/ (Online). Accessed in: 26-05-2017).Search in Google Scholar
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