Deep Boltzmann Machine based Breast Cancer Risk Detection for Healthcare Systems

One of the second major cause for tumour fatality among ladies globally is due to the occurrence of breast tumourand greater part of them passes away because of suspended diagnosis of the tumour. But early recognition and anticipation can fundamentally lessen the odds of death. Consequently, initial stage diagnosis of breast tumour is really a basic prerequisite to protect a patients' lifespan. By means of improving medical technologies, for breast tumour prediction, various tumour features have been gathered from Wisconsin Breast Cancer Database (WBCD) of UCI repository. Separation of all the correlated feature data to support the medical syndrome identification is an inspiring, stimulating and time consuming task. To manage this issue, a model with deep learning techniques has been proposed to recognize breast tumour at prime stage. The proposed system makes use of Deep Boltzmann Machine (DBM) for finding an efficient set of features and Deep Neural Network (DNN) classifier is used to classify the women either into benign group or malignant group. The proposed framework is evaluated using specificity, sensitivity and accuracy with the classifiers like support vector machine (SVM), combined neural network (CNN), multilayer perceptron neural network (MLPNN), probabilistic neural network (PNN), recurrent neural network (RNN), Naïve Bayes (NB), SMO and C4.5 used in the existing system for breast tumour classification.

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