Breast cancer remains one of the leading causes of mortality among
women globally, emphasizing the need for early and precise diagnostic
systems. Traditional machine learning models, while effective, often
function as black boxes, offering limited interpretability to healthcare
professionals. Despite advancements in diagnostic tools, there remains
a gap in delivering models that are both highly accurate and
explainable. Existing models tend to prioritize predictive performance
over transparency, making it difficult for clinicians to trust and adopt
them in real-world scenarios. This work proposes a Fuzzy Rule-Based
Modelling (FRBM) approach for breast cancer prediction that
balances accuracy with interpretability. The proposed system translates
numerical input data into linguistic fuzzy sets and derives inference
rules using a Sugeno-type fuzzy inference system. Feature selection is
carried out using a combination of correlation-based methods and
expert knowledge to ensure only relevant diagnostic attributes are used.
The model generates understandable IF-THEN rules, providing
clinicians with clear decision logic. The dataset used is the Wisconsin
Diagnostic Breast Cancer (WDBC) dataset from the UCI repository.
The proposed fuzzy model achieved an accuracy of 97.6%,
outperforming traditional models such as Support Vector Machines
(SVM) and Decision Trees (DT), which achieved 94.8% and 93.5%,
respectively. Additionally, the fuzzy system demonstrated a high F1-
score of 0.96 and excellent interpretability, enabling users to
understand and validate predictions.
S. Savitha1, V. Umadevi2
K.S.R. College of Engineering, India1, Arunai Engineering College, India2
Breast Cancer Prediction, Fuzzy Rule-Based System, Interpretability, Explainable AI, Medical Diagnosis
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| Published By :
ICTACT
Published In :
ICTACT Journal on Soft Computing
( Volume: 16 , Issue: 1 , Pages: 3763 - 3768 )
Date of Publication :
April 2025
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