Objective: Breast cancer is a significant healthcare challenge in Iran, imposing both human and economic burdens. This study focuses on reducing healthcare costs by improving early diagnosis and management. By leveraging advanced machine learning techniques, the research aims to predict diagnosis delays and identify risk factors specific to Iranian breast cancer cases. These insights can streamline diagnostic processes, optimize resource allocation, and ultimately lessen the economic impact of breast cancer management in Iran.
Methods: Methodology outlines the creation of machine learning frameworks for predicting breast cancer diagnosis delays and risk factors. Utilizing algorithms like Random Forest, Bagged CART, Neural Networks, and Logistic Regression, alongside ensemble methods, the chapter covers data preprocessing, feature selection, and parameter tuning. Evaluation techniques such as Confusion Matrix, Sensitivity Evaluation, Specificity Analysis, and AUC Analysis assess model performance, aiming to improve diagnostic accuracy and inform effective management strategies.
Results: The analysis identified critical variables for predicting breast cancer diagnosis delays and risk factors across various machine learning models. While age emerged as a universal factor, the importance of variables like urban residency and marital status varied. Lifestyle, health history, and familial background emerged as significant predictors, highlighting the intricate nature of risk assessment. Similarly, permutation feature importance analysis revealed consistent predictors such as chest X-ray history and deliberate weight loss for overall breast cancer risk. These findings inform personalized risk assessment and intervention strategies, enriching our comprehension of breast cancer prediction.
Conclusion: this study's findings provide crucial insights into predicting breast cancer diagnosis delays and risk factors in Iranian cases. By employing advanced machine learning techniqu