Hybrid Deep Learning with TSOA Model for Predicting the Parkinson Disease using Speech Features

True early identification of Parkinson’s illness (PD) is absolutely fundamental to stopping the ill health course and planning affected role with access to disease-modifying medications. To do this, significant attention should be paid to the premotor stage of PD. Based on premotor characteristics, a unique deep-learning algorithm is provided to swiftly detect whether or not a person has PD. By identifying and assessing indicators that indicate speech attribute and degree of worsening, a voiced numerical biomarker may supplement the laborious traditional physical test. The project attempts to develop a revolutionary end-to-end deep learning model using speech data for Parkinson’s disease identification. The current study employs a mixed deep learning model to evaluate and identify PD. The study’s objectives were to identify vocal anomalies in PD patients and create an automatic test technique that can distinguish concerning PD patients’ voices and those of fit informs while also grading voice quality. his study investigates the effectiveness of utilizing deep learning architectures for speech data analysis in the context of [insert context]. Specifically, we employ a convolutional neural network (CNN) architecture with recurrent layers (e.g., Long Short-Term Memory, LSTM) to extract hierarchical features from raw speech signals. Our methodology involves preprocessing the raw audio data to extract relevant acoustic features, such as mel-frequency cepstral coefficients (MFCCs) and spectrograms. These features are then fed into the CNN-LSTM model, which learns discriminative representations for various speech tasks, including [mention specific tasks, e.g., speech recognition, emotion recognition, etc.].The category accurateness was evaluated using the Italian PVS speaking quantities and the Lithuanian PD voice dataset. With values of 0.92 showing that the results are medically adequate, the findings demonstrated that the suggested model is highly generalizable.