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...

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Principais autores:	Nihar Ranjan Panda, R. Rajalakshmi, Surapati Pramanik, Mana Donganont, Prasanta Kumar Raut
Formato:	Artigo
Idioma:	Inglês
Publicado em:	University of New Mexico 2025-07-01
coleção:	Neutrosophic Sets and Systems
Assuntos:	breast cancer diagnosis machine learning neutrosophic environment neutrosophic numbers predictive analytics medical decision-making
Acesso em linha:	https://fs.unm.edu/NSS/48BreastCancer.pdf
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