Advanced Deep Learning Methodologies in the Diagnosis of Parkinson's Disease: A Comprehensive Review

This study of the literature explores the field of sophisticated deep techniques for diagnosing Parkinson’s illness and severity evaluation. The research looks into the use of machine learning algorithms as potential markers of Parkinson’s illness in manual illustrations and speech impairments. These algorithms include XGBoost, Neural Networks with Recurrence, and ensemble models. Numerous feature extraction techniques, model comparison assessments, and preprocessing methodologies are investigated in an effort to improve precision and efficacy of Parkinson’s illness diagnosis. In order to improve performance and interpretability of the prototype, the importance of feature engineering, ensemble learning techniques, and interpretability techniques like LIME and SHAP values is emphasized. The study also highlights how crucial it is to integrate several models for real-time monitoring systems to assist in the prompt identification and ongoing tracking of Parkinson’s illness. Future possibilities for examine include adding other symptoms including handwriting distortion and olfactory sound loss, investigating new algorithms, and using deep learning approaches to improve system performance. Considerable progress in Parkinson’s disease diagnosis and therapy can be made by filling up these research gaps and utilizing deep learning. By addressing these problems via additional study and technical developments, PD can be detected and managed earlier and more effectively, which will ultimately improve patient quality of life.