Research Article
Performance analysis of different machine learning algorithms in breast cancer predictions
@ARTICLE{10.4108/eai.28-5-2020.166010, author={Gopi Battineni and Nalini Chintalapudi and Francesco Amenta}, title={Performance analysis of different machine learning algorithms in breast cancer predictions}, journal={EAI Endorsed Transactions on Pervasive Health and Technology: Online First}, volume={}, number={}, publisher={EAI}, journal_a={PHAT}, year={2020}, month={8}, keywords={Machine learning, feature selection, tumor classification, accuracy, AUC}, doi={10.4108/eai.28-5-2020.166010} }- Gopi Battineni
Nalini Chintalapudi
Francesco Amenta
Year: 2020
Performance analysis of different machine learning algorithms in breast cancer predictions
PHAT
EAI
DOI: 10.4108/eai.28-5-2020.166010
Abstract
INTRODUCTION: There is a great percentage of failures in clinical trials of early detection of breast cancer. To do this, machine learning (ML) algorithms are useful to do diagnosis and prediction of cancer tumors with better accuracy.
OBJECTIVE: In this study, we develop an ML model coupled with limited features to produce high classification accuracy in tumor classification.
METHODS: We considered a dataset of 569 females diagnosed as 212 malignant and 357 benign types. For model development, three supervised ML algorithms namely support vector machines (SVM), logistic regression (LR), and K-nearest neighbors (KNN) were employed. Each model was further validated by 10-fold cross-validation and performance measures were defined to evaluate the model outcomes.
RESULTS: Both SVM and LR models generated 97.66% accuracy with total feature evaluation. With selective features,the SVM accuracy was improved by 98.25%. Whereas the LR model including limited features produced 100% of true positive predictions.
CONCLUSION: The proposed models involved by selective features could improve the prediction accuracy of a breast cancer diagnosis.
Copyright © 2020 Gopi Battineni et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.