Machine learning techniques with ANOVA for the prediction of breast cancer

Abstract

Breast cancer is one of the most common cancer among females. In this paper, machine learning techniques are applied to molecular taxonomy of breast cancer international consortium (METABRIC) dataset to extract prime clinical attributes to get high accuracy. Analysis of variance (ANOVA), the statistical method, is used for clinical feature selection. Five different machine learning algorithms are implemented, which are support vector machine (SVM), decision tree, random forest, AdaBoost and artificial neural network (ANN). Among all the machine learning classifiers, ANN gives the highest accuracy of 87.43%. This statistical technique is helpful for the detection of breast cancer, and it will increase the survival rate of females.

A Deep Learning Methodology for Plant Species Recognition Using Morphology of Leaves

“…One of the significant impacts of deep learning lies in its effective feature extraction methods. Manual feature extraction processes require extensive expertise in the agricultural and ecological domains, and they are also time-consuming [11][12][13][14][15].…”