Indian Journal of Science and Technology

Abstract

Objectives: The major motivation is to build the prediction model for diagnosis. The fundamental exploration of prediction is to anticipate breast cancer at a prior stage that guarantees a long survival of patients. Methods/Statistical Analysis: In medical field, the classification of tissues surrounding the malicious cancer cells into benign and malignant categories is extremely challenging task to predict. For diagnosis of a disease, Naive Bayesian [NB], Support Vector Machine [SVM] and Artificial Neural Network [ANN] Classification systems are investigated and Fuzzy C-Means Clustering are analyzed to make clusters. Fuzzy C-Means Clustering [FCM] algorithm clusters the data with simulated annealing which is classified using the above mentioned classifiers in furtherance of developing best prediction model with predefined rules. The performance is validated with K-fold cross validation. Findings: The Wisconsin Breast Cancer Dataset [WBCD] from UCI dataset storehouse is utilized to test the execution of classifiers. This dataset holds 10 properties with 699 records. This dataset has been clustered as benign and malignant. In the clusters, to achieve global optima simulated annealing technique is used and the classifiers are applied for clusters. In this examination, Fuzzy C-Means Clustering [FCM] with simulated annealing and Naive Bayesian classifier serves to be the best one with 89.2% accuracy and its F-measure is computed as 0.9417. The various performance metrics are computed for proposed novel model and its results are compared with existing values which indicates, the Naive Bayesian classifier works well for non-dependency data as there is no affinity between attributes and is considered as most noteworthy among them. Application/Improvements: Prediction model can be used for predicting any disease in medical field domain, which can be further improved by using Farthest First Clustering [FFC] algorithm.

Keywords: Artificial Neural Network [ANN], Breast Cancer, Classification, Fuzzy C-Means Clustering [FCM], Naive Bayesian [NB], Semi-Supervised