Breast-cancer identification using HMM-fuzzy approach.
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.
Department of Computer Science and Software Engineering, The University of Melbourne, Victoria 3010, Australia. mrhassan@csse.unimelb.edu.au
Md Rafiul
MR
Hassan
M Maruf
MM
Hossain
Rezaul Karim
RK
Begg
Kotagiri
K
Ramamohanarao
Yos
Y
Morsi
D000465:Algorithms; D019540:Area Under Curve; D001943:Breast Neoplasms / Q000175:diagnosis*; D005260:Female; D017143:Fuzzy Logic; D006801:Humans; D016013:Likelihood Functions; D008962:Models, Theoretical; D012372:ROC Curve