The invention discloses a method for detecting Parkinson severity by using speech based on clustering, which comprises the following steps: 1, collecting speech signal; 2, preprocessing speech signals; 3. Extracting all speech features, including pitch, Jitter, Shimmer, SNR and nonlinear features. 4, model and calculation; and 5, carrying out prediction: loading classification and regression modelfor each classification of clustering; getting the classification result; the severity of the disease was predicted from the marker values, and feeding back the predicted results to the front-end through the interface and presented to the user. The invention solves the problem that there is no fixed index in clinic to determine whether there is Parkinson's disease or not, and also solves the problems of long clinical observation Parkinson's cycle and high cost, and has the characteristics of real-time, high efficiency and low cost.
Method for detecting Parkinson severity by using speech based on clustering