<![CDATA[As Japan’s population ages, the demand for home medical care is increasing. And, as the demand for home medical care increases, the burden on medical personnel becomes a problem. One of the ways to promote home medical care is to spread the use of medical equipment in households. However, since some medical knowledge is required to use basic medical equipment, it is considered difficult to spread the use of medical equipment among general households. Therefore, it demands the necessity to develop a medical device that can give the same decision as a medical doctor by using an algorithm. In this paper, we study the construction of an algorithm for an AI stethoscope that can make the same decisions as a medical doctor. We combine a frequency analysis method using three features and an image processing method using an image that represents the frequency features by wavelet transform. Using the results of each of these methods, we aim to improve the identification rate through machine learning techniques. The Random Forest training yields an identification rate of 94.68 ％ on the dataset of this paper.]]>
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