| Summary: | 碩士 === 國立陽明大學 === 衛生資訊與決策研究所 === 95 === The stroke is a cardiovascular disease. The annual incidence of stroke is approximately 700,000 per year in the United States, and Ischemic stroke accounts for 80% of them. In Taiwan 51,000 people suffer from stroke each year, and 15,000 of them resulted in death. From 1993 to 2005 stroke is the second most common cause of mortality, and 71% was due to ischemic stroke in Taiwan. In America, the annual cost on health care of stroke is near 40 billion USD, while in Taiwan the monthly cost is 15-40 thousand NTD. That was to make the nation, the patients and their family’s financial and psychological burden. So, the win-win situation could not be accomplished to the patient and nation. Therefore, if information techniques can be used to predict the hospitalization length of stay of ischemic stroke patients. And to discover the effect variables of ischemic stroke length of stay. It can provide for the medical personnel to make the medical plan. We hold that can expect the increased usage of acute wards and decrease the cost of medical expenses.
In our study used the 441 patient data records and 14 variables. The variables are included age, sex, Diabetes Mellitus, Hypertension, heart disease, smoking, alcohol, hyperlipemia, old CVA, LOS, admission and discharge BI score and admission of NIHS score. This study applied C4.5 decision tree, ANN and logical regression with past data to establish the prediction model and to predict the patients’ length of stay.
In the analysis from the C4.5 decision tree one can see that the key variables in the LOS of ischemic stroke patients are BI score, age, hyperlipidemia, sex, alcohol, HTN, heart disease, NIHSS at admission hospital, in which the BI during hospitalization is the most important. We found that the C4.5 decision tree model out performs ANN and logistic regression in terms of accuracy. So, it points out if applying the C4.5 decision tree and ANN to predict the ischemic stroke length of stay, it has high of predictive accuracy. In future studies the model should be applied in clinical practice.
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