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Parkinson's disease is a degenerative disease that leads to brain disorder and nonfunctioning of different body parts. Deep learning tools like artificial neural network (ANN), convolution neural network (CNN), regression Analysis (RA), and so on, has been considered to a great extent in recent days. Several data sets based on the motor and nonmotor symptoms are applied to different classifier for correct identification of Parkinson's patient from healthy people. In this paper, hybridization of two deep learning tools such as, RA and ANN are done for effective diagnosis of the disease by probability estimation. The communal merits of individual approaches of the existing approaches are realized in this context for accurate probability estimation. Data preprocessing and probability estimation of preprocessed data is done in RA. The second existing approach is used to identify the PD patient by comparing with a predefined threshold value of a neuron. The estimation is performed on the data set of speech recognition, iron content, and pulse rate among a group of people. The proposed approach is compared with the existing approaches like, SVM and k‐NN classifier. The computed result reveals the superiority of the proposed algorithm with 93.46% accuracy. [ABSTRACT FROM AUTHOR]