Predicting Breast Cancer using Machine Learning Classifiers and Enhancing the Output by Combining the Predictions to Generate Optimal F1-Score
Copyright policy )Predicting Breast Cancer using Machine Learning Classifiers and Enhancing the Output by Combining the Predictions to Generate Optimal F1-Score
Background: Biomedical field has gained a lot of interest from active researchers today. Treating various diseases prevailing among the world has believed to bring huge insight in the today's research world. Second, advancement in technology has eased the work of researchers to justify their work. Machine learning (ML) is an approach being used by bioengineers today to predict diseases and to even aid them in drug discovery. Methods: Considering both the points, one of the most serious diseases, that is breast cancer here, is predicted using ML approaches. Breast cancer is classified as either benign or malignant which is to be predicted with the help of ML classifiers. A very famous dataset Wisconsin Breast Cancer Dataset is used here and is trained by three classifiers mainly support vector machine, general linear model, and neural network (NNET) against testing dataset. Testing the breast cancer prediction was carried out keeping in mind the accuracy of each of the classifiers. Results: This study is involving a generic code in R language. Conclusions: The study intends to show the usage of NNETs in breast cancer prediction using single-layered structure.
receiver operating characteristic, accuracy, neural network, wisconsin dataset, r, general linear model, machine learning classifiers, breast cancer, support vector machine, f1-score, TP248.13-248.65, Biotechnology
receiver operating characteristic, accuracy, neural network, wisconsin dataset, r, general linear model, machine learning classifiers, breast cancer, support vector machine, f1-score, TP248.13-248.65, Biotechnology
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