Research Article
Neural Networks Classification for Breast Cancer Analysis
@INPROCEEDINGS{10.4108/eai.12-10-2019.2296520, author={Paquita Putri and Intan Nurma Yulita}, title={Neural Networks Classification for Breast Cancer Analysis}, proceedings={Proceedings of the 7th Mathematics, Science, and Computer Science Education International Seminar, MSCEIS 2019, 12 October 2019, Bandung, West Java, Indonesia}, publisher={EAI}, proceedings_a={MSCEIS}, year={2020}, month={7}, keywords={breast cancer neural networks zeror multilayer perceptron}, doi={10.4108/eai.12-10-2019.2296520} }- Paquita Putri
Intan Nurma Yulita
Year: 2020
Neural Networks Classification for Breast Cancer Analysis
MSCEIS
EAI
DOI: 10.4108/eai.12-10-2019.2296520
Abstract
Cancer is caused by a lump from a collection of cells that grows and attacks the surrounding tissue. Most breast lumps are benign, but benign breast lumps can increase the risk of developing breast cancer. Early detection can provide better handling. The diagnosis of breast cancer by a doctor is done by analyzing several factors. To help the doctor in diagnosing the data efficiently, this study implemented machine learning. The diagnosis was based on the features of digital image computation in the process of fine-needle aspiration (FNA) of a breast mass. The data came from 569 patients. The study used the neural networks classification method with multilayer perceptron algorithms. The results were obtained that the use of neural networks gave higher accuracy if it compared it in the ZeroR method. Their accuracies were 95.96%, and 62.74%, respectively.
