Comparing with the Application of SVM, RF, and MLP in Stroke Prediction

• Main Authors: Mei-Ci Tu, 凃美綺
• Other Authors: Sun-Jen Huang
• Format: Others
• Language: zh-TW
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/r83228

碩士 === 國立臺灣科技大學 === 資訊管理系 === 107 === Stroke is the second cause of death in the world. Stroke not only causes physical discomfort in the human body, but subsequent care treatment also causes a huge burden on society and the family. It also seriously affects the quality of life in the future. In order to avoid the occurrence of stroke, people only pay attention to the risk factors related to stroke and control it from the usual routine, and people can effectively prevent the occurrence of stroke. Nowadays, machine learning and deep learning technology have been widely used in the medical field. Among them, the research on brain stroke is mostly the image data of medical examination, and it is modeled using the image algorithm. However, medical examination data is not easy to obtain. Moreover, when it is actually used in the future, it is also necessary for the patient to go to the hospital for examination, through this information to predict. Therefore, the model is less convenient in practical applications and requires additional medical resources. The inspection is not only an economic burden on the patient but also increases the incidence of acute renal function decline in the patient and increases a burden on the country. This study employed the data from the Behavioral Risk Factor Surveillance System, and collected the stroke-related risk factors as the basis for the selection of variables. It further established a set of SVM, RF, and MLP of stroke prediction models, and constructed a set of model training and verification process and model evaluation process. The results of each model evaluation were compared based on the model evaluation indicators and then the best model of each evaluation was selected according to each evaluation indicator. It was found that the MLP was the best performance in the accuracy evaluation. SVM was the best performance in the sensitivity evaluation. RF was the best performance in the specificity evaluation. The modeling and evaluation methods proposed in this study can provide a reference for future research. The information system can also be developed based on the constructed model in this study.