A Study on the Assessment of the Gallstone Disease Associated with Stroke
| Autor: | Wei-Cheng Chien, 簡瑋成 |
|---|---|
| Rok vydání: | 2016 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 104 Gallstone disease and Stroke are public health problems of the modern times. Research analysis on the relationship between gallstone disease and Stroke remains scarce. Past studies have considered the cholesterol deposit in the gallbladder, hardening of the arterial wall, excessive cholesterol such as hypercholesterolemia and hyperlipidemia, and other conditions such as hypertension, diabetes, peripheral vascular disease, chronic kidney disease, alcoholism, and chronic pulmonary disease conditions to be recognized risks factors for Stroke. In this study, gallstone disease patients from the database of an anonymous medical institution were adopted as the research participants. Through literature reviews and interviews with physicians the important factors contributing to increased risk of accompanying Stroke were screened. Then, using the artificial intelligence particle swarm optimization algorithm, genetic logistic regression algorithm cross-entropy algorithm, and case-based reasoning system, the factor weights were calculated in order to construct predictive models and case-based assessment system and assess whether or not the risk of accompanying Stroke disease existed. Findings show that among the six predictive models that underwent statistical testing, particle swarm optimization algorithm, the genetic logistic regression algorithm, cross-entropy algorithm coupled with support vector machine was the best combination. The Friedman test verified the predictive model accuracy between the models was significant, indicating difference existed between the models but a significant area under ROC curve. The paired sample t-test was then conducted to determine the superiority of the models. After determination, particle swarm optimization algorithm coupled with the support vector machine was the best predictive model. However, through the k-fold validation, the average accuracy and the area of ROC under curve all reached over 89% and 0.89. The optimized parameters were conjunctively used to carry out stroke classification. The cross-entropy algorithm, coupled with back-propagation network, BPN and support vector machine, SVM derived at a better accuracy rate and the area under the ROC curve of 98%, 98% and 0.95 respectively, which were deemed most suitable for stroke classification. As for the accompanying Stroke disease system assessment, though the k-fold validation, three algorithms coupled with the case-based reasoning had average accuracy and average area under ROC curve reaching above 93% and 0.85. The assessment models verified by the Friedman testing had significant accuracy and area under ROC curve, indicating differences existed between the models. The paired sample t-test was then adopted to analyze the superiority of each model set. Among them, particle swarm optimization algorithm was the best. The average accuracy and the area of ROC under curve all reached over 94% and 0.91. The research results shall serve as a reference for medical institutions or clinical workers during disease diagnosis and assessment and provide patients with early treatment and prevention in order to lessen the load of patients with disease. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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