Abstract
The amount of data that human beings can use increase numerously and lead to the rising of research for big data with many results like the enhancement of operating speed in computers, the advance of storage technology, the development of communications technology, etc. With the rising of research for big data, it also results in the development of data mining, making human beings get valuable information from big data. If the research for big data can be applied to medical field, the research that would achieve to improve care, save lives and lower costs benefits human beings a lot. However, using sequential data processing tool of one general machine costs numerous time with the increasingly growing amount of data. It leads to other problems for too slow time with numerous time costs. If a framework for distributed parallel computing can be used to process data with lots of machines, it will reduce computing time sharply. In the past, much research points out that intake of SSRIs which is commonly prescribed for treatment of depression or related mental health conditions has increased risk of stroke. Based on these research results, the thesis research uses National Health Insurance Research Database to analyze the healthcare data and builds machine learning model for predicting stroke with people of SSRI intake in the past. Using Hadoop, a tool of distributed computing, speeds up data processing. Compared to the previous work in our lab, the same group enhances approximately 35 times in preprocess and approximately 585 times in extracting features for speed. The effect of enhancement is obvious. The processed data use GBDT as classifier for analysis to build machine learning model. It is able to extract more features with the obvious enhancement of processing speed. By examining the top 20 most important features, the final result demonstrates that our model show more risk factors compared to the previous work in our lab, and it may possess valuable clinical information.