| Summary: | 碩士 === 國立虎尾科技大學 === 工業管理系工業工程與管理碩士班 === 104 === Tuberculosis, TB and liver disease have long been diseases of worldwide prevalence. TB is also the leading cause of death among the infectious diseases around the world. From past experience, patients with cirrhosis are more susceptible to TB, but studies on the correlation between cirrhosis and TB remain scarce. This study adopted patients with cirrhosis above the age of 18 from the database of an anonymous domestic medical institution as research participants. Through relevant literatures and interviews with physicians and after selecting the important factors likely to cause TB, the particle swarm optimization algorithm, PSO, genetic algorithm with logistic regression, and cross-entropy algorithm were adopted to calculate the weights of the factors. The back-propagation network, BPN and support vector machine, SVM were conjunctively used to construct predictive models and case-based reasoning systems to evaluate whether patients with cirrhosis have accompanying TB. Research results show that although the PSO combined with the case-based reasoning system has the best accuracy, after the k-fold verification, the average accuracy, ACC was 91.52% and area under the ROC curve, AURC was 0.917. The Friedman’s test shows no significant difference, thus indicating no difference exists between the models. Therefore, the models are all suitable for calculating the evaluation system weights. Among the six predictive models, although the PSO combined with the BPN are the best, after the k-fold verification, the average ACC was 91.22% and AURC was 0.833. The Friedman’s test shows no significant difference, thus indicating no difference exists between the models and that they can all be used in predictive calculations. The research results shall serve as a reference for medical institutions or clinical workers during aided diagnosis, thereby achieving “early detection and early treatment”, relieving patients of the burden of disease, and ensuring timely treatment during the golden time.
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