Analysis and Classification of Biomedical Data Using Machine Learning Techniques

Abstract

Breast cancer research has been the center of discussion on health information technology in recent years, as it is the second largest cause of cancer deaths in women. A biopsy in which tissue is microscopically extracted and analyzed will be used for the detection of breast cancer. The cancer cells are either categorized as cancer or non-cancer. There are various methods for classifying and predicting breast cancer. The proposed work uses a breast cancer dataset from Wisconsin (Diagnostic). The data collection used to track the effectiveness of different machine learning approaches with respect to main parameters including accuracy, F1-score, specificity and recall. The three basic classification models in this paper are i.e., Naïve Bayes, KNN, Decision Tree was used to forecast cancer. The comparative tests show that the proposed Naïve Bayes classifier gives the accuracy of 98.1%, f1 score of 98.3%, specificity of 97.5%, recall of 98.3%.