Diagnosis and treatment of breast cancer have been improved during the last decade; however, breast cancer is still a leading cause of death among women in the whole world. Early detection and accurate diagnosis of this disease has been demonstrated an approach to long survival of the patients. As an attempt to develop a reliable diagnosing method for breast cancer, we integrated support vector machine (SVM), k-nearest neighbor and probabilistic neural network into a complex machine learning approach to detect malignant breast tumour through a set of indicators consisting of age and ten cellular features of fine-needle aspiration of breast which were ranked according to signal-to-noise ratio to identify determinants distinguishing benign breast tumours from malignant ones. The method turned out to significantly improve the diagnosis, with a sensitivity of 94.04%, a specificity of 97.37%, and an overall accuracy up to 96.24% when SVM was adopted with the sigmoid kernel function under 5-fold cross validation. The results suggest that SVM is a promising methodology to be further developed into a practical adjunct implement to help discerning benign and malignant breast tumours and thus reduce the incidence of misdiagnosis. Diagnosis and treatment of breast cancer have been improved during the last decade; however, breast cancer is still a leading cause of death among women in the whole world. Early detection and accurate diagnosis of this disease has been demonstrated an approach to long survival of the patients. As an attempt to develop a reliable diagnostic method for breast cancer, we integrated support vector machine (SVM), k-nearest neighbor and probabilistic neural network into a complex machine learning approach to detect malignant breast tumor through a set of indicators consisting of age and ten cellular features of fine-needle aspiration of breast which were ranked according signal-to-noise ratio to identify determinants distinguishing benign breast tumors from malignant ones. The method turned out to significantly improve the diagnosis, with a sensitivity of 94.04% , a specificity of 97.37%, and an overall accuracy up to 96.24% when SVM was adopted with the sigmoid kernel function under 5- fold cross validation. The results suggest that SVM is a promising methodology to be further developed into a practical adjunct implement to help discerning benign and malignant breast tumors and thus reduce the incidence of misdiagnosis.