Construction of Bayesian Low Back Pain Risk Assessment System

• Main Author: 江鴻屏
• Other Authors: Chien-Lung Chan
• Format: Others
• Language: zh-TW
• Published: 2004
• Online Access: http://ndltd.ncl.edu.tw/handle/99844214508784572471

碩士 === 元智大學 === 資訊管理研究所 === 92 === 　The computational capability of information technology helps people in many areas. In medical domain, the application of information technology such as data mining or data warehousing becomes more and more general. And they are useful in illness forecast and analysis. Low back pain (LBP) is unlike cancer that will cause death. But LBP does influence patients’ quality of life greatly. This causes total burden of illness (TBI). To help patient understand the risk factors and help patient change their behavior. This is the key to reduce the probability of LBP occur. 　This study collects LBP risk-factor data in questionnaire and chooses naïve bayes classifier to be a classification on LBP from many data mining technologies (decision tree, neural network, Bayesian classifier). We collect 900 subject data and import these data to naïve bayes classifier in training process. The sensitivity of this model is 76%, and the specificity is 84%. The area of ROC curve is close to 0.88. This result shows that naïve bayes classifier is a good model in LBP risk assessment. 　This study uses naïve bayes classifier to evaluate the probability of LBP. And construct a web-based LBP risk evaluative system. User can input their risk data to evaluate their probability of LBP. This help user understand their risk factor and the benefit of changing behavior. They also can change their risk factor dynamically to know that if they change their behavior early they can reduce the probability of LBP occur. This web-based system provides a friendly interface to teach people understand the importance of LBP.