Explaining Predictions of Hypertension Disease through Anchors

Hypertension is a disease that stresses the arteries and can cause damage to vital organs. It is often asymptomatic, and timely diagnosis and management are crucial to prevent complications and mitigate the risks associated with the disease. Photoplethysmography has proven to be effective in capturing variations in blood volume within vessels and holds the potential for continuous monitoring of heartrelated diseases to be adopted in real-time systems [1]. Using automated processing on “high-risk” medical data requires careful attention to regulations. The emergence of Explainable Artificial Intelligence (XAI) is especially important in this context because it can provide explanations that clarify the reasoning behind the results produced by automatic processing. This paper introduces the application of an agnostic algorithm called Anchors for explaining predictions related to hypertension levels through the use of concatenations of logic statements. This algorithm has been selected based on its ability to produce easily understandable explanations, which is particularly valuable in the medical domain, where the primary stakeholders are physicians and patients. Additionally, it has been chosen for its ability to balance classification and explanation accuracy. Furthermore, we have investigated the impact of varying the number of features utilized in the explanations on the quantitative measures. This exploration involved the application of diverse feature selection methods, and their outcomes were systematically compared. Experiments showed that reducing the number of features does not harm classification performance and significantly improves the quality of explanations