Translating Visually the Reasoning of a Perceptron: The Weighted Rainbow Boxes Technique and an Application in Antibiotherapy

In this paper, we propose a technique for translating visually the reasoning of a perceptron. The artificial neuron, or perceptron, is a simplified model of a biological neuron. It can achieve simple reasoning and solve linearly separable problems. Despite its limited reasoning power, it is enough to deal with several real-life problems. The proposed technique is based on rainbow boxes, a technique for overlapping set visualization, which has been applied to the input vectors of the perceptron. We extended this technique, leading to weighted rainbow boxes. It can visualize several input vectors and output values for a single perceptron. We applied this approach to decision support in antibiotherapy, for the determination of the most appropriate antibiotic in urinary infections, by taking into account the properties of each drug (e.g. efficacy, risk of adverse effects, etc). Finally, a user study with 11 physicians showed that most of them found the visualization interesting and easy to read.