ระบบพยากรณ์การเป็นโรคไฮโปไทรอยด์โดยใช้เหมืองข้อมูล

Abstract

Project HYPOTHYROID DISEASE PREDICTION SYSTEM USING DATA MINING Author Mr. Panuphong Jenrotphondet Mr. Ausron Binmaduereh Major Computer Science Advisor Asst. Prof. Dr. Orasa Patsadu Academic Year 2023 Abstract This project develops Hypothyroid disease prediction system using data mining. The objective of this project is to compare performance of classification technique such as Artificial neural network, Support vector machine, Decision tree to consider high accuracy of model for risk estimation of Hypothyroidism. The dataset is used to build model from UCI. There are 2800 rows and 21 attributes. The result of performance estimation found that Decision tree is accuracy of 99.5%. Artificial neural network and Support vector machine are accuracy of 96.28 and 92.85, respectively. This project uses Decision tree model to predict Hypothyroidism for decision support of physician to predict Hypothyroidism and show data visualization to show primary knowledge about Hypothyroidism for user and estimate risk of Hypothyroidism.