Work

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

Year: 2025

Type: article

Abstract: 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.

Source: Entropy

DOI: 10.3390/e27020162

Authors: Barbara Pękala, Dawid Kosior, Wojciech Rząsa, Katarzyna Garwol, Janusz Czuma

Institutions: University of Information Technology and Management in Rzeszow, University of Rzeszów

Language: English

Topic: Artificial Intelligence in Healthcare

Subfield: Health Information Management

Field: Health Professions

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,137