With an aggravated aging population, Parkinson disease has become a neurodegenerative disease affecting millions of elderly people, therefore, it is crucial to establish a companion diagnostic system that can provide timely and accurate diagnostic results for patients with Parkinson disease. To address the problems of misdiagnosis and under-diagnosis of Parkinson disease, an auxiliary diagnosis system for Parkinson disease (MI-GA) based on mutual information and genetic algorithm is proposed. In the feature extraction process, genetic algorithm is used to approximate the unimportant features in the clinical features of patients. And the mutual information is introduced to assign weights for each clinical feature, and then the weights are applied to the KNN algorithm to achieve the accuracy of the diagnosis results. The experimental performance indicated that the method can significantly alleviate the noise data disturbance of the clinical features of Parkinson disease and improve the accuracy of Parkinson's disease diagnosis.