This study investigated methods for accurately determining the appropriate timing for changing ostomy appliance using image processing and machine learning. In ostomy users, if urine or stool leaks during use and enters the space between the stoma and the ostomy appliance, the appliance may detach or leak. Therefore, changing ostomy appliance at the appropriate time is critically important. However, nurses and patients unfamiliar with changing ostomy appliance may misjudge the timing for replacement. In the first phase of this study, we attempted to identify the suitable timing for stoma appliance replacement using image processing and machine learning. Specifically, we implemented the following steps: 1. converted color images to grayscale images, 2. reduced the resolution of grayscale images, 3. applied a Gaussian filter, 4. locally optimized the kernel size and standard deviation, and 5. locally optimized the number of epochs. In the second phase, we demonstrated that augmentation of misclassified data improved prediction rates in both the timing requiring replacement and timing not requiring replacement. Furthermore, the false negative rate, a standard evaluation metric in machine learning, decreased by 10%, and the F1 score increased by 2.6 for the timing requiring replacement and by 1.4 for the timing not requiring replacement.