EVALUATION OF INTELLIGENT SYSTEM TO THE CONTROL OF DIABETES

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Please use this identifier to cite or link to this item: http://ir.cmu.edu.tw/ir/handle/310903500/43876

Title:	EVALUATION OF INTELLIGENT SYSTEM TO THE CONTROL OF DIABETES
Authors:	(SHOU-WEI CHIEN);(CHO-TSAN BAU);(KER-CHUNG LIN);(KUO-AN WAN);陳永福(Yung-Fu Chen);(JONG-CHEN CHEN)
Contributors:	公共衛生學院醫務管理學系
Keywords:	Diabetes;Logistic regression analysis;Clinical decision support system;Neural network;Support vector machine;Recursive feature elimination;Decision tree;Radial basis function network
Date:	2012-01
Issue Date:	2012-04-19 15:07:51 (UTC+8)
Abstract:	Diabetes, which ranks 4th among the top ten causes of death in Taiwan, is one of the most important medical issues in the 21st century. However, for most diabetic patients, their blood sugar is not under good control, especially in ICUs. Good control of blood sugar may reduce the risk of sepsis to 34% for patients in ICUs. The importance of good blood sugar control for patients in ICUs is manifested by a signicant reduction of mortality and morbidity. This study designed a clinical decision support system (CDSS) using a support vector machine (SVM) to predict if a critically ill patient can have good glucose control after insulin administration. A lter method based on logistic regression analysis (LRA) and a wrapper method based on recursive feature elimination (RFE) were adopted to select salient features from 10 variables for CDSS design. Data on 231 patients (2492 records) were collected covering four years from an ICU. Four signicant variables (p < 0:05) using LRA in contrast to ve ones using RFE algorithm, were selected. The results show that the predictive accuracy under cross-validation was 93.50% for features selected with LRA, and the accuracy, sensitivity and specicity with SVM-RFE were 95.75%, 92.71% and 99.81%, respectively. It could predict the outcome quite accurately after having injected a certain dose of insulin. The proposed system may help doctors effectively assess their patients in determining insulin dose for better glucose control in an ICU setting.
Relation:	International Journal of Innovative Computing Information and Control 8(1(B)):779-790
Appears in Collections:	[Department and Graduate of Health Services Administration] Journal articles

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