Forecast the Onset of Parkinson’s Disease at all Three Stages using Deep Learning Techniques

The most common neurological ailment & the 2nd most frequent reason for disability & mortality is Parkinson's disease (PD). A difficult challenge for making sure that people can live as independently as feasible is the correct identification of PD in the initial stages. Over the past few decades, artificial intelligence-based machine learning techniques for disease identification in medicine have become very popular. These techniques do not, however, offer a precise and rapid diagnosis. The total detection precision of methods connected to machine learning was insufficient. Italian medical control individuals and PD sufferers, mid-advanced patients treated to L-Dopa, separated into non-medication patients and early-diagnosed, were used to create a custom dataset using high-quality recordings of vocal assignments. The goal is to construct an accurate model that can accurately diagnose to identify PD early and begin treatment while it is still treatable, early diagnostic tools are required. However, current models contain drawbacks that can cause the disease to be misdiagnosed. The Unified PDRating Scale (UPDRS), a grading system for PD, could be continuously monitored with the use of the proposed framework, which could aid clinicians treat patients. According to the various ML pipelines, state-of-the-art is contrasted using various categorization methods&DL was also investigated utilizing a unique Convolutional Neural Network (CNN) framework. Outcomes demonstrate that KNN, SVM, and Naive Bayes classifiers operate comparably to each other with KNN having a little advantage over SVM and DL in terms of categorization outcomes. The dominance of CFS as the top characteristic choice is much clearer. The chosen characteristics serve as pertinent vocal biomarkers that can distinguish between healthy people, early-stage PD patients who have not yet received treatment, and mid-advanced PD patients who have received L-Dopa. The proposed approach has a 0.10 RMSE error, which is less than that of current models and can identify PD. The recommended method can extract crucial vocal elements that can be used to identify PD early on.