Polat, KemalŞentürk, Ümit2020-04-302020-04-302018978-1-5386-4184-2https://hdl.handle.net/20.500.12684/24432nd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) -- OCT 19-21, 2018 -- Kizilcahamam, TURKEYWOS: 000467794200056Breast cancer is the second most common cancer in our country and in the world. In this study, a breast cancer data set was formed from the findings obtained from experiments conducted in the city of Coimbra of Portugal. There are two sets of data (52 data: healthy group, 64 data belong to patient group) and 9 features in the breast cancer data set of 116 data, both healthy and patient. These nine features are: Age, BMI, Glucose, Insulin, HOMA, Leptin, Adiponectin, MCP-1. In the proposed method, a three-step hybrid structure is proposed to detect the presence of breast cancer. In the first step, the dataset was first normalized by the MAD normalization method. In the second step, k-means clustering (KMC) based feature weighting has been used for weighting the normalized data. Finally, the AdaBoostM1 classifier has been used to classify the weighted data set. Only the combination the AdaBoostM1 classifier with MAD normalization method yielded a 75% classification accuracy in the detection of breast cancer, whereas the hybrid approach achieved 91.37% success. These results show that the proposed system could be used safely to detect breast cancer.<bold> </bold>eninfo:eu-repo/semantics/closedAccessBreast cancerMachine learningdiagnosisfeature weightingclassification<bold></bold>Conference Object315318N/A