英文摘要 |
As the lifestyle and eating habits of people nowadays changed, liver disease has not only caused great damage to people's health, but also been the top ten among the top ten leading causes of death for a long time. Because liver disease has no particularly significant characteristics at the initial stage, people cannot easily detect whether or not they have liver disease. However, if one can diagnose the pre-liver disease early, and give proper health education and treatment, it can delay it to become a serious liver cirrhosis disease. With the advancement of algorithms on intelligent computing, a variety of algorithms have been gradually applied to various disciplines in science. Therefore, this study proposes a fitness-based Whale Optimization Algorithm (WOA) to enhance the performance of the original WOA. Some experiments are performed using 13 benchmark functions by the proposed WOA and original WOA. Furthermore, this study conducts the data mining analysis of ILPD (Indian Liver Patient Dataset) reported in the UCI machine learning repository. The dataset of ILPD includes 583 instances and 11attributes. To classify the dataset of liver disease, we also use four data mining techniques included in the Weka software: J48, Naïve Bayes, Bayes Net, and Multilayer Perceptron. Moreover, the original and improved WOAs and the four aforementioned algorithms included in the Weka are evaluated for the classification analyses of ILPD dataset to compare the classified accuracy of the liver disease dataset. |