Integrating Traditional Diagnostics Methods Through Neutrosophic Set Based Cancer Analysis Using Machine Learning Techniques

This paper introduces a novel approach for utilizing Neutrosophic Sets through machine learning to enhance classification accuracy by incorporating truth, falsity, and indeterminacy in decision-making. Traditional cancer cell classification models struggle to handle indeterminate
cases effectively. A Neutrosophic Confusion Matrix (NCM) is developed to extend conventional performance evaluation metrics, considering the probability distribution of positive, negative, and neutral classifications. This framework enables a more comprehensive assessment of classification reliability, particularly in ambiguous cases where traditional machine-learning models exhibit limitations. The proposed approach for classification uses two significant imagery features: texture contrast and color saturation as well as neutrosophic sets that can effectively differentiate between benign, malignant, and healthy skin lesions through Machine Learning concepts. Through empirical validation of medical datasets, this work establishes Neutrosophic based classification as a powerful tool for improving the accuracy and robustness of cancer diagnosis.