This article aims to evaluate the prediction models of machine learning classification in terms of accuracy, objectivity, and reproducible of the diagnosis of malignant neoplasm with fine needle aspiration. Also, we seek to add one more class for testing in this database as recommended in earlier studies. We present six various classification methods: Multilayer Perceptron, Decision Tree, Support Vector Machine, Random Forest, and Deep Neural Network for evaluation. In the field of assisted cancer diagnosis, it's expected that the involvement of machine learning in diseases will give doctors a second opinion and help them to form a faster / better determination. There is an enormous number of studies in this area using traditional machine learning methods and in other cases, using deep learning for this purpose.