Breast-Cancer identification using HMM-fuzzy approach

• Published in: Computers in biology and medicine Vol. 40; no. 3; pp. 240 - 251
• Main Authors: Hassan, Md. Rafiul, Hossain, M. Maruf, Begg, Rezaul Karim, Ramamohanarao, Kotagiri, Morsi, Yos
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.03.2010; Elsevier Limited
• Subjects: Algorithms; Area Under Curve; Breast cancer; Breast Neoplasms - diagnosis; Classification; Feature selection; Female; Fuzzy Logic; Hidden Markov model (HMM); Humans; Internal Medicine; Likelihood Functions; Models, Theoretical; Other; Receiver operating characteristics (ROC); ROC Curve; Fuzzy logic; Hidden Markov model (HMM); Feature selection; Receiver operating characteristics (ROC); Classification
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2009.11.003

This paper presents an ensemble of feature selection and classification technique for classifying two types of breast lesion, benign and malignant. Features are selected based on their area under the ROC curves (AUC) which are then classified using a hybrid hidden Markov model (HMM)-fuzzy approach. HMM generated log-likelihood values are used to generate minimized fuzzy rules which are further optimized using gradient descent algorithms in order to enhance classification performance. The developed model is applied to Wisconsin breast cancer dataset to test its performance. The results indicate that a combination of selected features and the HMM-fuzzy approach can classify effectively the lesion types using only two fuzzy rules. Our experimental results also indicate that the proposed model can produce better classification accuracy when compared to most other computational tools.