## 2 Materials and Methods
### Data Canyons  
As the aim of this article is to present the symbiotic cooperation between computer science, medical expertise, and an agile approach to manipulation and improvement of the output machine learning model, we will only briefly introduce Data canyons to understand the underlying logic and the visualization concept. Data canyons are a novel machine learning approach with a human-interpretable output model. Because it is inherently interpretable, it aligns well with the concepts of XAI. As the name suggests, the concepts of Data canyons are derived from the natural phenomenon of river canyons. The idea of transferring nature's concepts into machine learning is not new; there are several concepts and algorithms of machine learning and AI that owe their existence to nature [14,15]. The base elements of the natural phenomenon, namely a river canyon, which were utilized in the machine learning algorithm were the river stream, the land mass, and time. The test data represent the river stream and are, throughout the algorithm, referred to as the data stream. The land mass is represented via a 3D surface plot where we represent the terrain of the canyon. What remains is the element of time which, in the case of Data canyons, is represented via the iterative processing of each data instance as a separate data stream in the given time frame. Data canyons are created for each class separately. For n classes, the output gives us n Data canyons. All Data canyons consist of sections that represent the sequential attributes from the train data and can be thought of as cross sections of the canyon that are sequentially equidistant. The terrain of the Data canyon sections is created for each attribute separately, measuring the occurrences of values between the maximum and minimum values of the current attribute. The bridging of the distance between adjoining attributes is created using the same principle as the attribute sections, except that the value is dictated by the connection between the two values of the adjoining attributes. There are two main variables that can be set to fine-tune the performance of the canyons, the length between adjoining attributes, and the width of the canyon. The length between attributes increases or decreases the emphasis on the importance of the connection between two parameters. Longer distance puts the emphasis on the connection between two parameters. A shorter distance puts the emphasis on the values of the sections. The width parameter controls the amount of detail the canyon should capture. Wider canyons focus on more detail, while narrow canyons focus on the higher abstraction of parameters. The difference in the changes of the output canyons, if the main attributes of the canyons are focused on length and width or are neutral, can be observed in Figures 2-4. The algorithm gives a visual output of the model in the form of a 3D surface plot where the depth is presented using gradient color changes to mimic the natural appearance of canyons. The depth of each Data canyon presents the occurrence of attribute values in the given area of the canyon. The width and depth are normalized to minimize the influence of unbalanced datasets on the final output. This visualization technique presents the end user with the ability to visually compare any given instance correlation to the output canyons in the form of a color-coded scatter plot. The scatter plot is similar to the canyon coded with a gradient except that there is an interchange from green to red and vice versa, where green presents a perfect fit to the canyon and red a complete mismatch. An example can be seen in Figure 5.  
Figure 4: Example of canyons where the length and width are neutral. Figure 3: Example of canyons where the width is prioritized. Figure 2: Example of canyons where the length is prioritized.