C4.5: programs for machine learning
C4.5: programs for machine learning
Discovering data mining: from concept to implementation
Discovering data mining: from concept to implementation
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Data Mining Techniques: For Marketing, Sales, and Customer Support
Data Mining Techniques: For Marketing, Sales, and Customer Support
Machine Learning
Expert Systems with Applications: An International Journal
Applying decision tree and neural network to increase quality of dermatologic diagnosis
Expert Systems with Applications: An International Journal
An expert system for detection of breast cancer based on association rules and neural network
Expert Systems with Applications: An International Journal
Comparing classification techniques for predicting essential hypertension
Expert Systems with Applications: An International Journal
Artificial Intelligence in Medicine
Improving medical decision trees by combining relevant health-care criteria
Expert Systems with Applications: An International Journal
Data mining applied to the cognitive rehabilitation of patients with acquired brain injury
Expert Systems with Applications: An International Journal
Review: Knowledge discovery in medicine: Current issue and future trend
Expert Systems with Applications: An International Journal
Developing a hybrid predictive system for retinopathy
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Hi-index | 12.06 |
Cerebrovascular disease has been ranked the second or third of top 10 death causes in Taiwan and has caused about 13,000 people death every year since 1986. Once cerebrovascular disease occurs, it not only leads to huge cost of medical care, but even death. All developed countries in the world put cerebrovascular disease prevention and treatment in high priority, and invested considerable budget and human resource in long-term studies, in order to reduce the heavy burden. As the pathogenesis of cerebrovascular disease is complex and variable, it is hard to make accurate diagnosis in advance. However, in perspective of preventive medicine, it is necessary to build a predictive model to enhance the accurate diagnosis of cerebrovascular disease. Therefore, coupled with the 2007 cerebrovascular disease prevention and treatment program of a regional teaching hospital in Taiwan, this study aimed to apply the classification technology to construct an optimum cerebrovascular disease predictive model. From this predictive model, cerebrovascular disease classification rules were extracted and used to improve the diagnosis and prediction of cerebrovascular disease. This study acquired 493 valid samples from this cerebrovascular disease prevention and treatment program, and adopted three classification algorithms, decision tree, Bayesian classifier and back propagation neural network, to construct classification models, respectively. After analyzing and comparing classification efficiencies - sensitivity and accuracy, the decision tree constructed model was chosen as the optimum predictive model for cerebrovascular disease. In this model, the sensitivity and accuracy were 99.48% and 99.59%, respectively, and eight important influence factors of predicting cerebrovascular disease and 16 diagnosis classification rules were extracted. Five experienced cerebrovascular doctors assessed these rules, and confirmed them to be useful to the current clinical medical condition.