Unique Method for Prognosis of Risk of Depressive Episodes Using Novel Measures to Model Uncertainty Under Data Privacy

The research described in this paper focuses on key aspects of learning from data concerning the symptoms of depression and how to prevent it. The computer support system designed for that purpose combines data privacy protection from various sources and uncertainty modeling, especially for incomplete data. The mentioned aspects are key to real-life medical diagnostic problems. From among the different paradigms of machine learning, a federated learning-based approach was chosen as the most suitable to take up the challenge. Importantly, computer support in medical diagnostics often requires algorithms that are appropriate for processing data expressing uncertainty and that can ensure high-quality diagnostics. To achieve this goal, a novel decision-making algorithm is used that employs interval entropy measures based on the theory of interval-valued fuzzy sets. Such an approach enables one to take into account diagnostic uncertainty, express it exactly, and interpret it easily. Furthermore, the applied classification technique offers the possibility of a straightforward explanation of the diagnosis, which is a situation required by many physicians. The presented solution combines innovative technological approaches with practical user needs, fostering the development of more effective tools in mental health prevention.