Comparing the efficiency of models for cervical cancer screening การเปรียบเทียบประสิทธิภาพตัวแบบสำหรับการคัดกรองผู้ป่วยมะเร็งปากมดลูก
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ABSTRACT
This research aims to improve the efficiency of models for cervical cancer screening. The data used in the study consists of cervical cancer patient data collected by Kelwin Fernandes, Jaime S. Cardoso, and Jessica Fernandes from Universidad Central de Venezuela, and is available on the website www.data.world.com. This data was then analyzed using the standard data mining process (CRISP-DM) with four classification techniques: Naïve Bayes, K-Nearest Neighbors, Decision Tree, and Random Forest. Performance metrics for data classification included Accuracy, F-measure, Sensitivity, and Specificity. The test results revealed that the Decision Tree technique is the most suitable for building a model for cervical cancer screening, achieving the highest accuracy of 96.62%, an overall F-measure of 76.93%, a sensitivity of 87.50%, and a specificity of 97.26%.
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