A Framework for Susceptibility Analysis of Brain Tumours Based on Uncertain Analytical Cum Algorithmic Modeling

Area of Science:

• Medical Informatics
• Computational Intelligence
• Decision Support Systems

Background:

• Brain tumor susceptibility analysis is crucial for diagnosis and treatment planning.
• Existing methods face challenges in handling complex data and uncertainties.
• Resource-limited regions require efficient and reliable diagnostic tools.

Purpose of the Study:

• To develop a robust multi-attribute decision support mechanism for appraising brain tumor susceptibility.
• To utilize the fuzzy parameterised complex intuitionistic fuzzy hypersoft set (FPCIFHSS) for enhanced uncertainty handling.
• To provide a generalized and reliable approach for assessing patient susceptibility to brain tumors.

Main Methods:

• Employed set-based operations of the fuzzy parameterised complex intuitionistic fuzzy hypersoft set (FPCIFHSS) model.
• Approximated brain tumor types using relevant symptoms based on expert opinions and complex intuitionistic fuzzy numbers (CIFNs).
• Computed tumor susceptibility scores by converting CIFNs to fuzzy values and using a core matrix.

Main Results:

• The FPCIFHSS model effectively handles information-based uncertainties and periodic data.
• Susceptibility degrees are represented by sub-intervals within [0, 1].
• The model provides a quantitative measure of patient susceptibility to different brain tumor types.

Conclusions:

• The proposed FPCIFHSS-susceptibility model offers a reliable and generalized approach for brain tumor susceptibility analysis.
• This technique is particularly beneficial for developing countries with limited healthcare resources.
• The method enhances decision-making capabilities in brain tumor diagnosis and management.