Derive Predictive Scoring by Nomogram of the Feature Selection from Logistic Regression Model

• Main Authors: Min-Yen Lin, 林旻延
• Other Authors: 翁昭旼 教授
• Format: Others
• Language: zh-TW
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/77194049536789773794

碩士 === 國立臺灣大學 === 醫學工程學研究所 === 100 === Most information system had been developed for many years, which various kinds of data are stored in the database system of computer. As various kinds of predictive model are booming. The applications of predictive model in sociology, economics and clinical medicine are commonly used. In clinical medicine, to estimate the patient''s risk of death or the probability of recurrence for particular disease is very important. The process of model construction may explore some unexpected factors which had been ignored. If the researchers want to retrieve some important information which had been hidden in mass data, particular methods for computing and analysis are need. For this purpose, several data mining algorithms are created. In the process of data mining, in order to enhancing generalization capability, speeding up learning process and improving model interpretability, feature selection becomes more and more in attention. For high dimensions database, feature selection is important way to reduce the redundant variables before processing the data mining. Taking above arguments, the main purpose of present research is to derive the predictive scoring function for patients’ outcome from nomogram which is a visualization tool extended from logistic regression model. We used the database of “CT finding and cranial surgery report” from NTUH as material, and apply several methods to obtain the feature subset : first, we use likelihood ratio to provide a summary of how many times more (or less) likely patients with the good outcome are to have that particular result than with poor outcome for each feature variables. After dropping the irrelevant features. Second, we apply the c-index selection to repeatedly calculate the c-index of the logistic regression model which drop a different feature variable a time, and select the subset with maximum c-index of the model. In this thesis, we also compare it with other common variable selection criterion of logistic regression model, and validate the predictive scoring by clinicians with expert knowledge.