Development of Regression-Based Mathematical Models for Early Detection of Lung and Breast Cancer Using Clinical Parameters
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Date
2025-07-30
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Abstract
Cancer diagnosis at an early stage is vital in enhancing better outcomes of treatment and prevention
of fatalities, particularly in low- and middle-income economies, such as that of Bangladesh, where
modernized diagnostic methods are less available. The proposed research aims to construct
regression models to detect lung and breast cancer in their early stages using clinically and
efficiently accessible diagnostic parameters. A study of 1,000 patients with lung cancer and 570
patients with breast cancer was conducted. The most important predictors were identified through
correlation matrix analysis to perform the feature selection. The Response Surface Methodology
(RSM) establishes both single-order and polynomial regression models to capture both linear and
nonlinear correlations between variables. ANOVA was used to test the statistical significance and
the predictive power of the models. In the case of lung cancer, factors like history of smoking,
genetic risk, and chronic lung disease were identified as 12 significant predictors. In the case of
breast cancer, characteristics such as radius, perimeter, concavity, and compactness turned out to
be influential. Separate datasets were used to verify the empirical validity and strength of the
models. The findings indicate that the second-order polynomial models performed better than the
linear models because they were able to capture the complex interaction of the variables. This
study demonstrates that modeling based on regressions offers a cost-efficient, interpretable, and
scalable approach to early cancer screening. These models present significant opportunities to
support clinical decision-making in resource-constrained settings and facilitate early intervention
in healthcare environments with limited resources. The innovative procedure has the potential to
make a significant contribution to the improvement of cancer screening procedures and the current
state of health in underserved communities, such as Bangladesh, by reducing dependence on costly
diagnostic equipment.
Keywords: Early detection, mathematical model, regression analysis, ANOVA, Response Surface
Methodology (RSM), Lung cancer, Breast cancer.