Breast cancer is a dangerous type of cancer that spreads into other organs over time. Therefore, medical studies are being done for the early diagnosis by means of the anthropometric data and blood analysis values besides the mammographic and histological findings. However, medical studies have identified only cancer-related values but the value ranges indicating the cancer have not been determined yet. Concurrently the automated diagnostic systems are being developed to assist medical specialists in biomedical engineering studies. The range of values or boundaries indicating the cancer are automatically determined in biomedical methods, but only the diagnostic result is presented. Because of this, biomedical studies don't provide enough opportunity for medical experts to evaluate the relationship between values and result. In this study, decision trees that is one of data mining method was applied to anthropometric data and blood analysis values to complete the mentioned deficiencies in breast cancer diagnosis aiming studies. The determined value ranges were also presented visually to medical experts understand them easily. The proposed diagnostic system has accuracy rate up to 90.52% and provides value ranges indicating the breast cancer as well as mathematically presents the relations between the values and cancer.Le texte complet de cet article est disponible en PDF.
Blood analysis and anthropometric value ranges indicating breast cancer were presented visually.
The decision tree was determined as the method to diagnose breast cancer with a success rate of 90.5.
Importance of each attribute was presented numerically to medical experts for a preliminary diagnosis of breast cancer.
Keywords : Breast cancer, Data mining, Obesity, Hormone, Blood analysis