[RETRACTED ARTICLE] Efficiency Comparison of Classification Methods for Kidney Disease
Keywords:
Kidney disease data analysis, Multilayer perceptron, Naïve Bayes, Decision TreeAbstract
This article has been retracted at the request of the Editor-in-Chief
The objective of this research is to apply data classification mining techniques to predict the risk of kidney disease and to compare their performance to identify the most efficient algorithm. Three techniques are employed: Multilayer Perceptron (Neural Networks), Decision Tree, and Bayesian Learning. The dataset used in this research is the early-stage chronic kidney disease dataset from the UCI repository, containing 400 records from the year 2015. Twenty-five factors are considered for analysis, and the model performance is compared using the 10-Fold Cross Validation method. The Weka software is used as the research tool. The results show that the neural network model is the most suitable, with an accuracy of 98.75%, a precision of 98.80%, a recall of 98.80%, and an F-measure of 99.90%.
References
Akkarapon, P., & Nipaporn, C. (2023). Efficiency Comparison of Classification Methods for Kidney Disease with Data Mining Techniques. Journal of Science Engineering and Technology LOEI Rajabhat University, 3(1), 1-17. https://ph02.tci-thaijo.org/index.php/JSET/article/view/247493/168624
Friedman, N., Geiger, D., & Goldszmidt, M. (1997). Bayesian network classifiers. Machine learning, 29, 131-163. https://link.springer.com/content/pdf/10.1023/A:1007465528199.pdf
Kamolthip, V. (2022). Epidemiology and review of measures to prevent chronic kidney disease. Division of Non Communicable Diseases. https://ddc.moph.go.th/uploads/publish/1308820220905025852.pdf
Nongyao, N. (2021). Performance Comparison of Cardiovascular Risk Prediction Models using Data Mining Algorithms. Journal of Science and Technology, 40(2), 137-147. https://scjmsu.msu.ac.th/pdfsplit.php?p=MTYyMjE3NzA5My5wZGZ8MTctMjc=
Rubini, L., Soundarapandian, P., & Eswaran, P. (2015). Chronic Kidney Disease. UCI Machine Learning Repository. https://doi.org/10.24432/C5G020
Surajit, N. (1999). Knowledge discovery in database on kohonen self-organizing map algorithm [Unpublished master dissertation, Mahidol University]. http://www.thaithesis.org/detail.php?id=44074#
Weka 3: Machine Learning Software in Java. (2023). Retrieved from https://www.cs.waikato.ac.nz/ml/weka/
Zorman, M., Masuda, G., Kokol, P., Yamanoto, R., & Stiglic, B. (2002). Mining Diabetes Database with Decision Trees and Association Rules. In Computer-Based Medical Systems, 2002. (CBMS 2002). Proceedings of the 15th IEEE Symposium, 134-139. https://sci-hub.se/https://doi.org/10.1109/CBMS.2002.1011367
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ข้อความที่ปรากฏในบทความแต่ละเรื่องในวารสารวิชาการเล่มนี้เป็นความคิดเห็นส่วนตัวของผู้เขียนแต่ละท่านไม่เกี่ยวข้องกับมหาวิทยาลัยราชภัฎร้อยเอ็ด และคณาจารย์ท่านอื่นๆในมหาวิทยาลัยฯ แต่อย่างใด ความรับผิดชอบองค์ประกอบทั้งหมดของบทความแต่ละเรื่องเป็นของผู้เขียนแต่ละท่าน หากมีความผิดพลาดใดๆ ผู้เขียนแต่ละท่านจะรับผิดชอบบทความของตนเองแต่ผู้เดียว
