A Research of Data Mining Applied to the Predictive Model of Fatty Liver

• Main Authors: Norman Yau, 姚志成
• Other Authors: wplee
• Format: Others
• Language: zh-TW
• Published: 2005
• Online Access: http://ndltd.ncl.edu.tw/handle/29573473197968225009

碩士 === 中原大學 === 資訊管理研究所 === 93 === Fatty liver disease is a subtle disease, and it usually can be diagnosed by ultrasonography.Many risk factors are attributable to fatty liver disease. However, the accuracy of these risk factors and the model of fatty liver disease screening are not well-established. It is essential to create a reliable model for fatty liver disease screening. The database of health examination in Taipei Shong-Shan Hospital was analyzed. It enrolled 2,230 persons from 2000 to 2004, and 29 routine tests were arranged for each person in health examination. Six parameters, including general physical examination, blood lipid profile, liver function, renal function, urinalysis, and complete blood count (CBC), were assessed by decision-tree mining algorithm, logistic regression, and neural network. Their accuracy and sensitivity were calculated and compared. In this study, we conclude that: 1. The decision-tree algorithm for fatty liver screening has an accuracy of 78%, and it is better than logistic regression; 2. The accuracy of decision-tree algorithm for moderate to severe fatty liver disease is 93%; 3. The cut points of six parameters in decision-tree algorithm are: body-mass index (BMI) > 24.18kg/m2; triglyceride > 92.5mg/dL; alanine aminotransferase (ALT) > 26.4 U/L; uric acid (UA) > 5.15mg/dL; RBC count > 4.675 x 1012/L; 4. BMI, age, blood pressure, and pulse rate are reliable parameters for predicting fatty liver disease. The accuracy is 76%; 5. BMI > 24.57 kg/m2 and ALT > 40 U/L had an approximately 100% accuracy rate for predicting moderate to severe fatty liver disease.