Optimize Neural Fuzzy Systems for High-Dimensional Breast Cancer Data Analysis: A Deep Learning Approach

Abstract

Accurate and timely analysis of breast cancer data is crucial for the successful deployment and advancement of intelligent healthcare systems. Traditional health status prediction methods, which often rely on shallow models, fall short in complex clinical scenarios and are still unsatisfying for many real-world applications. This situation has inspired us to propose a deep learning-enhanced framework for health data flow prediction. The paper introduces a new three-layer soft computing method for predicting health status using optimizing neural fuzzy systems (ONFS). This approach enhances interpretability by considering spatial correlations in medical data. We start with feature selection based on the Pearson correlation coefficient (PCC) to eliminate variables with minimal linear or nonlinear relationships. Next, subtractive clustering optimization is applied in each layer to refine the system parameters simultaneously. The ONFS offers clearer and more straightforward explanations of health features in high-dimensional data analysis. Experimental results demonstrate the superiority of ONFS over existing methods, achieving an average RMSE reduction of 17.2% and a 98% reduction in rules compared to SVM, with competitive computational efficiency. This research underscores the potential of deep learning-augmented ONFS in enhancing breast cancer data analysis, supporting the information science objectives of precision and interpretability in healthcare data processing.