Performance Enhancement and Accuracy of Artificial Neural Networks Using Particle Swarm Optimization for Breast Cancer Prediction - Jimmy Nganta Ginting; Ronsen Purba; Erwin Setiawan Panjaitan (InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan, 2020)
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.