Fuzzy Soft Set Theory based Expert System for the Risk Assessment in Breast Cancer Patients

TL;DR

This paper introduces a fuzzy soft set theory-based expert system that assesses breast cancer risk using routine blood analysis parameters, aiding early detection and decision-making.

Contribution

It develops a novel fuzzy soft set-based model integrating clinical parameters for non-invasive breast cancer risk assessment.

Findings

The system accurately classifies high-risk patients.

Uses accessible blood parameters for risk estimation.

Supports early diagnosis and clinical decision-making.

Abstract

Breast cancer remains one of the leading causes of mortality among women worldwide, with early diagnosis being critical for effective treatment and improved survival rates. However, timely detection continues to be a challenge due to the complex nature of the disease and variability in patient risk factors. This study presents a fuzzy soft set theory-based expert system designed to assess the risk of breast cancer in patients using measurable clinical and physiological parameters. The proposed system integrates Body Mass Index, Insulin Level, Leptin Level, Adiponectin Level, and age as input variables to estimate breast cancer risk through a set of fuzzy inference rules and soft set computations. These parameters can be obtained from routine blood analyses, enabling a non-invasive and accessible method for preliminary assessment. The dataset used for model development and validation was obtained from the UCI Machine Learning Repository. The proposed expert system aims to support healthcare professionals in identifying high-risk patients and determining the necessity of further diagnostic procedures such as biopsies.