تشخیص سرطان پستان با استفاده از برآورد ناپارمتری چگالی احتمال مبتنی بر روش‌‌های هسته‌ای

Introduction: Breast cancer is the most common cancer in women. An accurate and reliable system for early diagnosis of benign or malignant tumors seems necessary. We can design new methods using the results of FNA and data mining and machine learning techniques for early diagnosis of breast cancer which able to detection of breast cancer with high accuracy. Materials and Methods: In this study, 699 samples of benign and malignancy with 9 characteristics from WBCD and 569 samples of benign and malignancy with 30 characteristics from WDBC were used. Then, a model based on non-parametric kernel density estimation is proposed for classification of WBCD and WDBC data. Results: The results of non parametric methods showed that Gaussian kernel method based on Euclidean distance with accuracy ٪97.93 has the highest accuracy on WDBC data and Gaussian kernel based on Euclidean distance and k-nearest neighbor methods with accuracy ٪98.17 has the highest accuracy compared with other methods on WBCD data for breast cancer disease. Conclusion: The result of this study showed that non-parametric kernel density estimation based classification can be used for breast cancer diagnosis with high accuracy.