Development and Performance Comparison of Models for Screening Randomly At-Risk Patients with Kidney Disease Using Data Mining Techniques การพัฒนาและการเปรียบเทียบประสิทธิภาพของตัวแบบสำหรับการคัดกรองผู้ป่วยกลุ่มที่สุ่มเสี่ยงเป็นโรคไต ด้วยเทคนิคการทำเหมืองข้อมูล
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Kidney disease is a major global public health problem that greatly affects patients' quality of life and imposes high treatment costs, especially in low- and middle-income countries. Studies have shown that the prevalence of chronic kidney disease stages 3-5 in Thailand is 12.4%, which is close to the global average but has been increasing continuously. Early screening for kidney disease is crucial in slowing down kidney deterioration and reducing complication rates. This study aimed to develop a model for screening individuals at risk of kidney disease using data mining techniques and to compare the performance of three data classification algorithms: Decision Tree, Random Forest, and K-Nearest Neighbors (KNN). A dataset of 1,500 records from a public database, including both at-risk and not-at-risk individuals for kidney disease, was used. The results showed that the KNN technique with a k value of 11 yielded the highest performance, with an accuracy of 89.20% and an overall F-measure of 17.35%, demonstrating its potential for accurate risk group screening. The findings of this research are beneficial for developing clinical decision support systems to assist healthcare professionals in identifying at-risk individuals and planning appropriate care, which can lead to reduced treatment costs and improved long-term quality of life for kidney disease patients.
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