Hypertension risk factors recognition by decision-tree approaches

• Main Authors: Chia-Yi Chang, 張嘉毅
• Other Authors: Yen-Hung Kuo
• Format: Others
• Language: zh-TW
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/21226730155926403913

碩士 === 嘉南藥理科技大學 === 醫務管理系 === 101 === Research purpose: This study aims to apply decision tree approaches to build a model of hypertension risk factors recognition. Based on the perspective of web of causation on hypertension, we adopted the hypertension risk factors in the literatures as the structure nodes with the application of creating the decision tree approach. Methods: Research sample were enrolled based on the 2005 National Health Interview Survey (NHIS) whose participants with the newly diagnosed hypertension patients between 2005 and 2007. The data were collected by combining the NHIS sample and medication record of National Health Insurance Research Database (NHIRD) between 2005 and 2007. Data was analyzed by applying descriptive statistic approaches, CHAID algorithm, and Logistic regression approach. Results: A totally 7,548 participants were enrolled in the model. In the first step, we found 4 major rules of classifying the hypertension. The major risk factors were the age with a cut-point of 55 years old and BMI with the cut-point of overweight and obesity. In the second step, we found 11 sub-major rules classifying the 4 major rules with risk factors of education level, gender, regular physical activities, betel-chewing behavior, and drinking behavior. In the third step, we applied the CHAID algorithm with the sub-major rules as the structure nodes to create 3 rules of hypertension recognition. Finally, the model was evaluated by Logistic regression approach. Conclusions: With this model we conclude that the structure of recognizing hypertension risk factors in Taiwan include two major rules. The first rule is patients aged above 55 years old with obesity and lower education level can be predicated as higher risk group. The second rule is patients aged less than 55 years old with higher education level can be predicted as lower risk group.