Classification of Breast Cancer Using Support Vector Machine and Forward Selection - Leliana Harahap; Erwin Setiawan Panjaitan; Muhammad Fermi Pasha (Institute of Computer Science (IOCS), 2020)
Breast cancer represents about 12% of all new cancer cases and 25% of all cancers in women. Early detection and classification of cancer is essential to save a person's life. The causes of breast cancer are multi-factorial and involve family history, obesity, hormones, radiation therapy, and even reproductive factors. Each year, one million new women are diagnosed with breast cancer, according to a World Health Organization report, half of them will die, because it is usually too late when doctors detect cancer. After the selected variable is then evaluated based on certain criteria. If the first selected variable meets the criteria for inclusion, the selection continues. The procedure stops, if no other variables meet the entry criteria and adds the variables one by one. The accuracy of the Support Vector Machine is influenced by several factors, including the comparison of the amount of training data and test data adjusted for k-fold validation. In the comparison of training data and test data the resulting accuracy reaches 97.68% with a total composition of 345 training data (50%) and 345 test data (50%). In the tests carried out, the accuracy of Support Vector Machine and Forward Selection was obtained at 97.68%.