Performance Enhancement and Accuracy of Artificial Neural Networks Using Particle Swarm Optimization for Breast Cancer Prediction

Performance Enhancement and Accuracy of Artificial Neural Networks Using Particle Swarm Optimization for Breast Cancer Prediction

Breast Cancer, Backpropagation Algorithm,
Particle Swarm Optimization.

Breast cancer is the one of leading causes of death among the women in many parts of the
world. According to Global Cancer Observatory (GCO) data from WHO (2018) show that
approximately 58,256 (16,7%) cancer cases were found in Indonesia out of a total of 348,809
cancer cases. The number of breast cancer patients throughout the world reached 42.1 per
100,000 population on average death rate of 17 per 100,000 inhabitants.Various ways have
been used to find effective methods in the early detection of breast cancer. A prediction of
breast cancer in early stage is very important in the medical world, which allows them to
develop strategic programs that will help diagnose and reduce mortality rates from breast
cancer. Performance enhancement and accuracy of artificial neural networks using particle
swarm optimization is an effective solution for breast cancer prediction. The accuracy result
was found 70% for training data and 96.1% for 30% prediction in this study. Previous studies
only used the backpropagation algorithm to predict breast cancer and the result was 94.17%.
Compared with previous study, there is an increase of 1.93% in combining Backpropagation
with Particle Swarm Optimization.

Jimmy Nganta Ginting, Ronsen Purba, Erwin Setiawan Panjaitan

Perpustakaan Horizon karawang

2020

Fajar bagus W

PDF

Indonesia

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