## 2 State of the Art Review of Signal Analysis Based Approaches for Analyzing Parkinson's Disease  
Given the technological nature of the paper, this section aims to provide a technological review of numerous computer-driven methodologies utilized for technological Parkinson disease analysis and investigation, categorization, and screening. The great majority of patients diagnosed with PD have vocal performance impairment [22], which may often be one of the first signs of the onset of the diseases and there is a clear demand for objective symptom assessment and screening [24]. Speech faults caused by [PERSON]'s disease include, among other things, decreased voice loudness [25], mono pitch [26], and improper consonant articulation as was defined by [PERSON]'s team [27]. Recent research have created tools to distinguish PD from controls and track speech rehabilitation in PD using high-quality voice recordings [28; 29]. Recent research have also looked at the practicality and usefulness of employing smartphone technology [30] to help with the clinical diagnosis of [PERSON]'s disease, which expanded the use of speech data to include four more tests for dexterity, postural sway, gait, and response times [31]. Modern AI-powered screening was shown to be hardware-independent, yielding accurate findings. According to a research conducted by [PERSON] et al. [32], smartphones can identify speech problems in those who are at high risk of acquiring PD. Acoustic measurements such as fundamental frequency variability, pause interval duration, and speech timing rate extracted from spontaneous speech were sensitive enough to distinguish between groups and demonstrated a strong correlation and reliability between the professional microphone and the smartphone. [PERSON] et al. [33] found a similar reliable results when using the k-nearest neighbor (KNN) algorithm. [PERSON] et al. [34] also introduced an iPhone app to assess the PD patient's memory, tapping, speech, and walking. [PERSON] et al. [35] created an app to assess the feasibility of remotely monitoring PD symptoms, and to use an AI-based approach to distinguish between measures before and after medication. [PERSON] et al. [36] compared smartphone outcome metrics to traditional clinical practice with trials that substantially distinguished PD from healthy persons with \(p<0.005\). Regarding the applications of artificial intelligence (AI), recent research has shown that the use of machine learning techniques to identify PD from non-PD using extracted speech data is successful [37; 38]. [PERSON] et al. [39] developed a multi-classifier framework to distinguish Parkinson's disease patients from healthy controls. [PERSON] et al. in Refs. [30; 40] studied the link between the characteristics of speech signals and the motor impairment score in Parkinson's disease patients. [PERSON] et al. [41] created an autonomous feature extraction system to diagnose [PERSON]'s disease and follow its development. [PERSON] et al. [42] evaluated the severity of PD using audio recordings of the vocal function. Ensemble learning was also important in boosting PD classification accuracy. As demonstrated by [PERSON] et al. [43], stacking results of conventional classifiers are time-efficient, low-cost, and more accurate, with an achieved accuracy of 95-97%. While these and related studies show great classification accuracy, they have numerous limits as they progress upward. Small cohort sizes are a more well-known issue, as they limit the generalizability of conclusions to a wider and more diversified population. Some researches also disregarded the fact that a person with PD may have greater trouble pronuncione some words than others. As a result, summarizing the voice examination may result in the loss of vital information [44]. To overcome this issue, the categorization setting must take into consideration what has been communicated.