2025, Vol. 6, Issue 2, Part C
Soft set based approaches in medical diagnosis and healthcare decision support systems
Author(s): Jai Prabhat Singh and Aklesh Kumar
Abstract: Soft set theory, introduced by Molodtsov, has emerged as a powerful mathematical tool to handle uncertainties and imprecisions in real-world problems, particularly where classical models such as fuzzy sets or rough sets encounter limitations. In the domain of medical diagnosis and healthcare, decision-making often involves dealing with incomplete, vague, or uncertain patient information. Soft set based approaches provide a flexible framework that enables physicians and healthcare systems to analyze medical symptoms, test reports, and patient history with greater accuracy and reduced ambiguity. By modeling uncertain attributes and offering parameterization techniques, soft sets help in constructing effective diagnostic systems for diseases such as diabetes, cancer, and cardiovascular disorders. Moreover, soft set theory can be integrated with fuzzy sets, rough sets, and neural networks to enhance decision support systems, thereby improving the reliability of medical predictions. Its ability to classify, rank, and filter diagnostic outcomes makes it especially valuable in multi-criteria medical decision-making, where numerous symptoms and conditions need to be evaluated simultaneously. Recent studies demonstrate the use of hybrid soft set models in developing intelligent healthcare applications, including early disease detection, personalized treatment recommendation, and risk assessment. Therefore, soft set based approaches hold significant potential in strengthening modern healthcare systems by providing efficient, accurate, and computationally feasible solutions for diagnosis and medical decision support.
Pages: 473-476 | Views: 74 | Downloads: 20
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How to cite this article:
Jai Prabhat Singh and Aklesh Kumar. Soft set based approaches in medical diagnosis and healthcare decision support systems. Journal of Mathematical Problems, Equations and Statistics. 2025; 6(2): 473-476.
