Classification of malignant and benign tissue with logistic regression

Abstract

Detection of breast cancer is the preliminary phase in cancer diagnosis. So, classifiers with higher accuracy are always desired. A classifier with high accuracy offers very less chance to wrongly classify a patient of cancer. This research investigates the performance of a modified and improved version of the hypothesis used in the logistic regression. Both gradient descent and advanced optimization techniques are used for the minimization of the cost function. A weighting factor of β is assigned in the hypothesis which is a sigmoid function. The dependency of the weighting factor to the number of features, the size of the dataset and the type of optimization technique used are observed. The accuracy of breast cancer detection is improved significantly by appropriately choosing the value of β, which, is a function of both the number of features and the type of optimization techniques used. The obtained results are promising by providing a significant increment in accuracy, sensitivity, and specificity. Keywords: Breast cancer detection, Machine learning classifier, Logistic regression, Performance evaluation tests, Weighted sigmoid function