Fuzzy Set and Soft Set Theories as Tools for Vocal Risk Diagnosis

New mathematical theories are being increasingly valued due to their versatility in the application of intelligent systems that allow decision-making and diagnosis in different real-world situations. This is especially relevant in the field of health sciences, where these theories have great potential to design effective solutions that improve people’s quality of life. In recent years, several prediction studies have been performed as indicators of vocal dysfunction. However, the rapid increase in new prediction studies as a result of advancing medical technology has dictated the need to develop reliable methods for the extraction of clinically meaningful knowledge, where complex and nonlinear interactions between these markers naturally exist. There is a growing need to focus the analysis not only on knowledge extraction but also on data transformation and treatment to enhance the quality of healthcare delivery. Mathematical tools such as fuzzy set theory and soft set theory have been successfully applied for data analysis in many real-life problems where there is presence of vagueness and uncertainty in the data. These theories contribute to improving data interpretability and dealing with the inherent uncertainty of real-world data, facilitating the decision-making process based on the available information. In this paper, we use soft set theory and fuzzy set theory to develop a prediction system based on knowledge from phonoaudiology. We use information such as patient age, fundamental frequency, and perturbation index to estimate the risk of voice loss in patients. Our goal is to help the speech-language pathologist in determining whether or not the patient requires intervention in the presence of a voice at risk or an altered voice result, taking into account that excessive and inappropriate voice behavior can result in organic manifestations.