| Summary: | 碩士 === 國立臺灣科技大學 === 工業管理系 === 101 === Stroke has become a big threat of health for people worldwide, the death rate and
disable rate of stroke are both high. Therefore, how to prevent stroke and discover it is
an important issue now. The best way to examine and discover stroke is the brain
image examination and ultrasound, however, the price of these examinations is
relatively high. People won’t take these examinations if there is no advice from doctor
or no obvious symptom people feel. Consequently, we want to use normal healthy
examination that is cheaper and easy to take to be the basic of our research, using
hybrid data mining techniques to find the association between normal healthy
examination and stroke. And adding some suggestion to the normal healthy
examination report, hope to provide more information to the public.
We use the brain examination data from 2004 to 2011 to develop a
Stroke-Risk-Predicting-Assistance Model by BPN. First, we do the clustering under
sampling, and then find the relative feature by rough set theory, information gain and
gain ratio. Finally, we use Taguchi method to set the best parameter for BPN. The
Stroke-Risk-Predicting-Assistance Model can support doctor to give people some
advise whether to do the brain examination or not, And to maximum the value of
normal healthy examination. People can know their brain health state, and prevent or
cure the stroke as soon as possible.
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