| Summary: | 碩士 === 國立虎尾科技大學 === 工業工程與管理研究所 === 102 === The main purpose of the National Health Insurance (NHI) system is to enable every citizen within the same system to uphold the spirit of mutual assistance and risks sharing. However, the lack of NHI comprehensiveness has resulted in excessive waste of medical resources and extremely serious financial imbalance. In the face of rising NHI costs, the effective assessment of medical resource allocation is an important evaluation indicator for reducing an unnecessary waste of medical resources. The birth weight of the newborn and the number of gestation weeks of the mother have always been used to measure the health level of newborns. However, the mother’s genes, nutrients, antibodies, and physiology have a determining impact on the development and growth of newborns. The mother’s knowledge of known risk factors greatly affects the health level of the newborn and most medical resources are consumed. Early detection and treatment will reduce an unnecessary waste of resources.
Eight types of hospitalization prediction models were established in this study. Using genetic logistic regression algorithm combined with back propagation neural network, the predictive models constructed demonstrate the best predictive results,with the average test accuracy of 88.6% and the area under curve of ROC is 0.8873, The database system and the user interface of the case-based reasoning system were adopted in this study. As for the assessment system of hospitalization days, genetic logistic regression algorithm combine with the case-based reasoning system had the accuracy of 98.03%, and the similarity rate of 99.75%; As for the assessment system of hospitalization costs, cross entropy algorithm combine with the case-based reasoning system had the accuracy of 71.26%, and the similarity rate of 99.79%. The study findings shall serve as a reference for relevant medical institutions when engaging in clinical assistance assessment.
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