In this paper the accuracy of two machine learning algorithms including SVM and Bayesian Network are investigated as two important algorithms in diagnosis of Parkinson’s disease. We use Parkinson's disease data in the University of California, Irvine (UCI). In order to optimize the SVM algorithm, different kernel functions and C parameters have been used and our results show that SVM with C parameter (C-SVM) with average of 99.18% accuracy with Polynomial Kernel function in testing step, has better performance compared to the other Kernel functions such as RBF and Sigmoid as well as Bayesian Network algorithm. It is also shown that ten important factors in SVM algorithm are Jitter (Abs), Subject #, RPDE, PPE, Age, NHR, Shimmer APQ 11, NHR, Total-UPDRS, Shimmer (dB) and Shimmer. We also prove that the accuracy of our proposed C-SVM and RBF approaches is in direct proportion to the value of C parameter such that with increasing the amount of C, accuracy in both Kernel functions is increased. But unlike Polynomial and RBF, Sigmoid has an inverse relation with the amount of C. Indeed, by using these methods, we can find the most effective factors common in both genders (male and female). To the best of our knowledge there is no study on Parkinson's disease for identifying the most effective factors which are common in both genders.