Breast cancer tumor is one the tumor discussed, researched and thought over by many recent time philosopher for it malignant and benign nature through manual and natural language methods. Due to the severity of tumor and increasing rate medical science has accepted this challenge and tried to diagnose it on early stage. Manual methods are not giving the results for mammographic images and statistical reviews for treatment. This study tends to evaluate the performance of seven machine learning classification models such as: Artificial Neural Network (NN), Bayes Network (BN), k-Nearest Neighbors Algorithm (KNN), Decision Tree (DT), Random Forest (RF), Logistic Regression (LR) and Support Vector Machine (SVM) are used to diagnose breast cancer symptoms.
Ghufran Ullah, HaiYan (2020); Comparative performance analysis of machine learning models for breast cancer diagnosis; International Journal of Scientific and Research Publications (IJSRP)
10(01) (ISSN: 2250-3153), DOI: http://dx.doi.org/10.29322/IJSRP.10.01.2020.p9742