Advancing Breast Cancer Diagnosis: A Comprehensive Machine Learning Approach for Predicting Malignant and Benign Cases with Precision and Insight in a Neutrosophic Environment using Neutrosophic Numbers

Breast cancer is still among the deadliest diseases globally, and its detection in an early stage still represents a big challenge in medical diagnostics. This research suggests a complete machine learning framework to predict the probability of benign and malignant breast cancer cases with improved accuracy and interpretability. The work uses an established dataset, and for comparative analysis and for insights into the data distribution, statistical analysis is also incorporated. Four top machine learning algorithms are trained and evaluated with a series of performance measures such as accuracy, positive predictive value (PPV), negative predictive value (NPV), F1-score, etc. In order to compensate for inherent uncertainties and imprecise in clinical data, the paper proposes a neutrosophic logic with neutrosophic numbers for improved decision-making. The results show the efficacy of using machine learning with neutrosophic theory to enhance diagnostic accuracy and facilitate early intervention measures in the treatment of breast cancer.