@article{MAKHILLJEAS2020152319495,
    title = {The Application of Particle Swarm Optimization Algorithm to Increase the Accuracy of MLP
Neural Network for Prediction of Breast Cancer},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {15},
    number = {23},
    pages = {3732-3740},
    year = {2020},
    issn = {1816-949x},
    doi = {jeasci.2020.3732.3740},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2020.3732.3740},
    author = {Mohammad,Zohreh and},
    keywords = {MLP neural network,particle swarm optimization algorithm,Breast cancer,increasing the prediction accuracy},
    abstract = {Breast cancer usually begins from breast tissue
and progresses rapidly. The disease is the most common
cancer that women suffer from. With late diagnosis of
breast cancer, the likelihood of the relapse of the disease
is increased. The earlier breast cancer is diagnosed, the
greater the likelihood of successful treatment would be.
Also, if cancer is diagnosed in the early stages, the
likelihood of the relapse of cancerous tumors is decreased.
The presence of various symptoms and features of this
disease makes it difficult for doctors to diagnose. The
neural network provides the possibility of analyzing
patient’s clinical data for medical decision making. The
purpose of this study is to provide a model for increasing
the accuracy of prediction of breast cancer. In this study,
patient’s information has been collected from the standard
database of Mortaz Super Specialty Hospital of Yazd. The
medical records of 574 patients with breast cancer having
a total of 32 features have been investigated. Each patient
has been followed for at least one year. In order to
provide a model of prediction of breast cancer, particle
swarm optimization algorithm and MLP neural network
are used. The proposed model was compared with the
methods of the nearest neighbor, Naïve Bayes and
decision tree. The results show that the prediction
accuracy of the proposed model is equal to 0.966. Also,
for the methods of Naïve Bayes, decision tree and the
nearest neighbor, prediction accuracy is 0.91, 0.929 and
0.913, respectively. In predicting breast cancer, the
proposed model includes minimum error rate and
maximum accuracy and validity compared to other
models. Naïve Bayes method has maximum error rate and
minimum accuracy.}
    }