A data mining approach to classify serum creatinine values in patients undergoing continuous ambulatory peritoneal dialysis
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Continuous ambulatory peritoneal dialysis (CAPD) is a treatment used by patients in the end-stage of chronic kidney diseases. Those patients need to be monitored using blood tests and those tests can present some patterns or correlations. It could be meaningful to apply data mining (DM) to the data collected from those tests. To discover patterns from meaningless data, it becomes crucial to use DM techniques. DM is an emerging field that is currently being used in machine learning to train machines to later aid health professionals in their decision-making process. The classification process can found patterns useful to understand the patients’ health development and to medically act according to such results. Thus, this study focuses on testing a set of DM algorithms that may help in classifying the values of serum creatinine in patients undergoing CAPD procedures. Therefore, it is intended to classify the values of serum creatinine according to assigned quartiles. The better results obtained were highly satisfactory, reaching accuracy rate values of approximately 95%, and low relative absolute error values.
KeywordsData mining Knowledge extraction Chronic kidney diseases Continuous ambulatory peritoneal dialysis Serum creatinine Clinical decision support systems Weka Classification algorithms
This work has been supported by Compete POCI-01-0145—FEDER-007043 and FCT—Fundação para a Ciência e Tecnologia within the Project Scope UID/CEC/00319/2013.
- 3.Guyton, A. C., & Hall, J. E. (2006). Guyton and hall textbook of medical physiology. Amsterdam: Elsevier.Google Scholar
- 5.Davis, C. P., & Shield Jr., W. C. (2018). Creatinine (low, high, blood test results explained). https://www.medicinenet.com/creatinine_blood_test/article.htm#what_is_creatinine. Accessed 21 Jan 2019.
- 6.Mildred Lam, M. (2018). Kidney failure—Understanding end stage renal disease (ESRD). http://www.netwellness.org/healthtopics/kidney/kidney2.cfm. Accessed 21 Jan 2019.
- 7.Peake, M., & Whiting, M. (2006). Measurement of serum creatinine—Current status and future goals. The Clinical Biochemist Reviews, 27, 173–184.Google Scholar
- 8.Oliveira, P., Portela, F., Santos, M. F., Machado, J., Abelha, A., Silva, Á., & Rua, F. (2016). Optimization techniques to detect early ventilation extubation in intensive care units. In Advances in Intelligent Systems and Computing (AISC) (pp. 599–608). Cham: Springer.Google Scholar
- 10.Abernethy, M. (2010). Data mining with WEKA, Part 2: Classification and clustering. https://www.ibm.com/developerworks/library/os-weka2/. Accessed 21 Jan 2019.
- 15.Portela, F., Santos, M. F., Machado, J., Abelha, A., Rua, F., & Silva, Á. (2015). Real-time decision support using data mining to predict blood pressure critical events in intensive medicine patients. In Lecture Notes in Computer Science (LNCS) (pp. 77–90). New York: Springer.Google Scholar
- 16.Portela, F., Filipe Santos, M., Silva, A., Rua, F., Abelha, A., & Machado, J. (2014). Preventing patient cardiac arrhythmias by using data mining techniques. In 2014 IEEE Conference on Biomedical Engineering and Sciences (IECBES). IEEE (2014) (pp. 165–170).Google Scholar
- 17.Pereira, S., Portela, F., Santos, M., Machado, J., & Abelha, A. (2016). Predicting pre-triage waiting time in a maternity emergency room through data mining. In Lecture Notes in Computer Science (LNCS)—Smart Health. New York: Springer.Google Scholar
- 18.Oliveira, S., Portela, F., Santos, M. F., Machado, J., & Abelha, A. (2014). Predictive models for hospital bed management using data mining techniques. In Advances in Intelligent Systems and Computing (AISC) (pp. 407–416). New York: Springer.Google Scholar
- 19.Aqlan, F., Markle, R., & Shamsan, A. (2017). Data mining for chronic kidney disease prediction. In Industrial and Systems Engineering Research Conference (ISERC).Google Scholar
- 20.Sharma, S., Sharma, V., & Sharma, A. (2016). Performance based evaluation of various machine learning classification techniques for chronic kidney disease diagnosis. International Journal of Modern Computer Scienc, 4, 11–16.Google Scholar
- 21.Bala, S., & Kumar, K. (2014). A literature review on kidney disease prediction using data mining classification technique. International Journal of Computer Science and Mobile Computing, 37, 960–967.Google Scholar
- 23.Chawla, N. V. (2005). Data mining and knowledge discovery handbook. New York: Springer.Google Scholar
- 24.Vijayarani, S., & Muthulakshmi, M. (2013). Comparative analysis of bayes and lazy classification algorithms. International Journal of Advanced Research in Computer and Communication Engineering, 2, 3118–3124.Google Scholar
- 26.Horning, N. (2010). Random forests: An algorithm for image classification and generation of continuous fields data sets. In The International Conference on GeoInformatics for Spatial-Infrastructure Development in Earth & Allied Sciences 2010 (pp. 1–6).Google Scholar
- 27.Devasena, L. (2014). Comparative analysis of random forest, REP tree and J48 classifiers for credit risk prediction. In IJCA Proceedings on International Conference on Communication, Computing and Information Technology (pp. 30–36).Google Scholar
- 28.Breiman, L., & Cutler, A. (2018). Random forests—Classification description. https://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm. Accessed 21 Jan 2019.
- 29.Kalmegh, S. (2015). Analysis of WEKA data mining algorithm REPTree, SimpleCart and RandomTree for classification of indian news. International Journal of Innovative Science Engineering and Technology, 2, 438–446.Google Scholar