## 2 Literature Review  
Several researchers, as detailed in Table 1, have implemented machine learning and deep learning techniques to diagnose [PERSON]'s disease (PD). These methods include the analysis of vocal features and brain imaging, along with certain specific drawings like meanders, spirals, and waves. Deep learning has gained prominence for its remarkable precision in detecting early stages of PD, particularly in medical imaging applications. For instance, [PERSON] et al. [21] used the Synthetic Minority Over-sampling Technique (SMOTE) on 195 voice samples to artificially expand the size of their dataset. SMOTE involves creating additional data points to balance the dataset by amplifying the representation of the minority class. The goal of this oversampling was to form a new dataset resembling the original in-class distribution but with a greater percentage of minority-class samples. Following this, LSTM networks were utilized to enhance the categorization of the disease into specific types. [PERSON] et al. [22] also applied this oversampling strategy to classify [PERSON]'s disease using vocal signals. It is critical to recognize, though, that such sampling techniques can introduce noise into the dataset if the chosen samples do not accurately reflect the true distribution. In their study, 50% of the data were allocated for training and testing, but this method resulted in a relatively modest accuracy rate of 94.8%. [PERSON] et al. [23] conducted research employing a bi-directional LSTM deep learning model with two LSTM layers, consisting of 20 and 200 units, respectively. The study achieved a 75% accuracy and an 80% F1 score in their experiments. In a separate study, [PERSON] et al. 's [24] research involved a dataset from 20 patients and applied a CNN. Despite making 361 prediction errors, their model attained an accuracy of 88%. [PERSON] et al. [25] also focused on using voice signals to predict PD, employing an LSTM model. They collected their dataset from a hundred patients, split evenly between healthy individuals and those with PD. After processing the dataset and applying their deep learning model, they achieved a notable accuracy of 91%. [PERSON] et al. [26] proposed an enhanced version of the K-Nearest Neighbors (KNN) algorithm, incorporating entropy weight for the detection of PD. The results showed that the improved KNN algorithm, with its entropy weight consideration, demonstrated a significant increase in accuracy when compared to traditional methods. This finding highlighted the potential of the modified KNN approach in improving the accuracy of PD detection. [PERSON] et al. [27] introduced a novel method for classifying symptoms of PD using MRI scans. The primary objective of their study was to delve deeper into clinical data to enhance the effectiveness of artificial intelligence in detecting PD. The integration of these techniques resulted in a model that demonstrated exceptional performance across all classification tasks. This advancement underscores the potential of AI-driven methods in improving the diagnosis and understanding of PD. [PERSON] and [PERSON] [28] developed a unique multi-pooling technique for classification, known as the octopus-based method, which utilized eight different pooling methods. This approach was applied to solve three classification problems: Gender, PD, and a combined Gender and PD classification. For feature extraction and selection, the authors employed techniques such as Singular Value Decomposition (SVD) and Neighborhood Component Analysis (NCA). In comparison, KNN was found to be particularly effective in solving the PD classification problem with high accuracy. Remarkably, the authors were able to address all three classification challenges using only 32 features, demonstrating the efficiency and effectiveness of their novel approach. From the above literature, it has been concluded that PD is a debilitating neurological disorder that greatly impacts the quality of life of those affected.