Early Diagnosis Of Parkinson's Disease Research Articles

Different patterns of exosomal α-synuclein between Parkinson's disease and probable rapid eye movement sleep behavior disorder.

The insidious onset of Parkinson's disease (PD) makes early diagnosis difficult. Notably, idiopathic rapid eye movement sleep behavior disorder (iRBD) was reported as a prodrome of PD, which may represent a breakthrough for the early diagnosis of PD. However, currently there is no reliable biomarker for PD diagnosis. Considering that α-synuclein (α-Syn) and neuroinflammation are known to develop prior to the onset of clinical symptoms in PD, it was hypothesized that plasma total exosomal α-Syn (t-exo α-Syn), neural-derived exosomal α-Syn (n-exo α-Syn) and exosomal apoptosis-associated speck-like protein containing a caspase activation and recruitment domain (ASC) may be potential biomarkers of PD. In this study, 78 PD patients, 153 probable iRBD patients (pRBD) and 63 healthy controls (HCs) were recruited. α-Syn concentrations were measured using a one-step paramagnetic particle-based chemiluminescence immunoassay, and ASC levels were measured using the Ella system. It was found that t-exo α-Syn was significantly increased in the PD group compared to the pRBD and HC groups (p < 0.0001), whilst n-exo α-Syn levels were significantly increased in both the PD and pRBD groups compared to HCs (p < 0.0001). Furthermore, although no difference was found in ASC levels between the PD and pRBD groups, there was a positive correlation between ASC and α-Syn in exosomes. Our results suggest that both t-exo α-Syn and n-exo α-Syn were elevated in the PD group, whilst only n-exo α-Syn was elevated in the pRBD group. Additionally, the adaptor protein of inflammasome ASC is correlated with α-Syn and may facilitate synucleinopathy.

A novel feature extraction method using chemosensory EEG for Parkinson's disease classification

Parkinson’s disease (PD) is an incurable nervous system disease that affects millions of people all around the world. The loss of smell is one of the first symptoms that come into prominence in the early diagnosis of PD. The main motivation of this study is to provide a more accurate diagnosis in the early period of the disease using chemosensory electroencephalography (EEG) signals, which are difficult to study and also less studied. For this purpose, we proposed a hybrid feature extraction method called EEMD_VAR that combines Ensemble Empirical Mode Decomposition (EEMD) and Vector Autoregressive Model (VAR). In contrast to conventional feature extraction methods, the proposed method is to prevent arbitrary selection of features and to determine the number of features. The pre-processed EEG signals were decomposed using EEMD and the obtained intrinsic mode functions (IMFs) used as independent variables in VAR. The coefficients of the VAR model were employed as features in frequently used supervised classification algorithms. The performance metrics of the EEMD_VAR were compared to the performance metrics of the autoregressive (AR) model and Hjorth parameters. The maximum classification accuracy of the proposed method was 100% using artificial neural networks (ANN) in C2 electrode, while the AR method and Hjorth parameters only obtained a maximum of 72%. The other metrics also corroborate the proposed method's ability to perform well in the classification. In addition, the higher results from right side electrodes may lead to the conclusion that the right side of the brain is more sensitive to odor stimuli.

Nanozyme-based cascade SPR signal amplification for immunosensing of nitrated alpha-synuclein.

A self-assembled nanozyme of iron porphyrin mediated supramolecular modified gold nanoparticles (FpA) was fabricated to determine nitrated alpha-synuclein as the Tyr 39 residue (nT39 α-Syn) of a potential biomarker for early diagnosis of Parkinson's disease (PD). Mechanically, localized surface plasmon resonance (LSPR) and the mass effect caused by catalytic deposition of the nanozyme contributed to a cascade signal amplification strategy. The sensor allowed a signal amplification and selective nT39 α-Syn bioanalysis with a 1.34-fold enhancement by cascade amplified SPR signal and double specific recognition. The detection limit was 1.78ng/mL in the detection range of 7-240ng/mL. Benefiting from the excellent immunosensor, this method can distinguish healthy people and PD patients usingactual samples. Overall, this strategy provides a nanozyme-based biosensing platform for the early diagnosis of PD and can be applied to detect other protein biomarkers, such as PD-L1.

Application of Neuromelanin MR Imaging in Parkinson Disease.

MRI has been used to develop biomarkers for movement disorders such as Parkinson disease (PD) and other neurodegenerative disorders with parkinsonism such as progressive supranuclear palsy and multiple system atrophy. One of these imaging biomarkers is neuromelanin (NM), whose integrity can be assessed from its contrast and volume. NM is found mainly in certain brain stem structures, namely, the substantia nigra pars compacta (SNpc), the ventral tegmental area, and the locus coeruleus. Another major biomarker is brain iron, which often increases in concert with NM degeneration. These biomarkers have the potential to improve diagnostic certainty in differentiating between PD and other neurodegenerative disorders similar to PD, as well as provide a better understanding of pathophysiology. Mapping NM in vivo has clinical importance for gauging the premotor phase of PD when there is a greater than 50% loss of dopaminergic SNpc melanized neurons. As a metal ion chelator, NM can absorb iron. When NM is released from neurons, it deposits iron into the intracellular tissues of the SNpc; the result is iron that can be imaged and measured using quantitative susceptibility mapping. An increase of iron also leads to the disappearance of the nigrosome-1 sign, another neuroimage biomarker for PD. Therefore, mapping NM and iron changes in the SNpc are a practical means for improving early diagnosis of PD and in monitoring disease progression. In this review, we discuss the functions and location of NM, how NM-MRI is performed, the automatic mapping of NM and iron content, how NM-related imaging biomarkers can be used to enhance PD diagnosis and differentiate it from other neurodegenerative disorders, and potential advances in NM imaging methods. With major advances currently evolving for rapid imaging and artificial intelligence, NM-related biomarkers are likely to have increasingly important roles for enhancing diagnostic capabilities in PD. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 2.

Parkinson’s Disease Classification and Clinical Score Regression via United Embedding and Sparse Learning From Longitudinal Data

Parkinson's disease (PD) is known as an irreversible neurodegenerative disease that mainly affects the patient's motor system. Early classification and regression of PD are essential to slow down this degenerative process from its onset. In this article, a novel adaptive unsupervised feature selection approach is proposed by exploiting manifold learning from longitudinal multimodal data. Classification and clinical score prediction are performed jointly to facilitate early PD diagnosis. Specifically, the proposed approach performs united embedding and sparse regression, which can determine the similarity matrices and discriminative features adaptively. Meanwhile, we constrain the similarity matrix among subjects and exploit the l2,p norm to conduct sparse adaptive control for obtaining the intrinsic information of the multimodal data structure. An effective iterative optimization algorithm is proposed to solve this problem. We perform abundant experiments on the Parkinson's Progression Markers Initiative (PPMI) data set to verify the validity of the proposed approach. The results show that our approach boosts the performance on the classification and clinical score regression of longitudinal data and surpasses the state-of-the-art approaches.

Parkinson’s disease detection based on multi-pattern analysis and multi-scale convolutional neural networks

Parkinson’s disease (PD) is a complex neurodegenerative disease. At present, the early diagnosis of PD is still extremely challenging, and there is still a lack of consensus on the brain characterization of PD, and a more efficient and robust PD detection method is urgently needed. In order to further explore the features of PD based on brain activity and achieve effective detection of PD patients (including OFF and ON medications), in this study, a multi-pattern analysis based on brain activation and brain functional connectivity was performed on the brain functional activity of PD patients, and a novel PD detection model based on multi-scale convolutional neural network (MCNN) was proposed. Based on the analysis of power spectral density (PSD) and phase-locked value (PLV) features of multiple frequency bands of two independent resting-state electroencephalography (EEG) datasets, we found that there were significant differences in PSD and PLV between HCs and PD patients (including OFF and ON medications), especially in the β and γ bands, which were very effective for PD detection. Moreover, the combined use of brain activation represented by PSD and functional connectivity patterns represented by PLV can effectively improve the performance of PD detection. Furthermore, our proposed MCNN model shows great potential for automatic PD detection, with cross-validation accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve all above 99%. Our study may help to further understand the characteristics of PD and provide new ideas for future PD diagnosis based on spontaneous EEG activity.

Multiple step saccades in simply reactive saccades could serve as a complementary biomarker for the early diagnosis of Parkinson’s disease

ObjectiveIt has been argued that the incidence of multiple step saccades (MSS) in voluntary saccades could serve as a complementary biomarker for diagnosing Parkinson’s disease (PD). However, voluntary saccadic tasks are usually difficult for elderly subjects to complete. Therefore, task difficulties restrict the application of MSS measurements for the diagnosis of PD. The primary objective of the present study is to assess whether the incidence of MSS in simply reactive saccades could serve as a complementary biomarker for the early diagnosis of PD.Materials and methodsThere were four groups of human subjects: PD patients, mild cognitive impairment (MCI) patients, elderly healthy controls (EHCs), and young healthy controls (YHCs). There were four monkeys with subclinical hemi-PD induced by injection of 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) through the unilateral internal carotid artery and three healthy control monkeys. The behavioral task was a visually guided reactive saccade.ResultsIn a human study, the incidence of MSS was significantly higher in PD than in YHC, EHC, and MCI groups. In addition, receiver operating characteristic (ROC) analysis could discriminate PD from the EHC and MCI groups, with areas under the ROC curve (AUCs) of 0.76 and 0.69, respectively. In a monkey study, while typical PD symptoms were absent, subclinical hemi-PD monkeys showed a significantly higher incidence of MSS than control monkeys when the dose of MPTP was greater than 0.4 mg/kg.ConclusionThe incidence of MSS in simply reactive saccades could be a complementary biomarker for the early diagnosis of PD.

Functional brain changes in Parkinson's disease: a whole brain ALE study.

Resting-state functional magnetic resonance imaging (rs-fMRI) was widely used as an effective tool in the diagnosis of neurodegenerative diseases. However, prior rs-fMRI studies reported inconsistent results for comparison between Parkinson's disease (PD) and healthy controls (HC). We searched studies published before December 2021 in databases (PubMed, Web of Science, and Google Scholar). An activation likelihood estimation (ALE) meta-analysis was made for functional changes in PD. The study finally included 25 studies (including 973 PD patients and 766 HC). PD patients showed reduced amplitude of low frequency fluctuations (ALFF) in the left superior temporal gyrus (STG), the left superior frontal gyrus (SFG), the left medial frontal gyrus (MFG), the left precuneus (PCUN), and the right lentiform nucleus, compared to HC. PD patients showed increased ALFF in the right SFG, the left superior parietal lobule (SPL), the left STG, the right fusiform gyrus, the left inferior temporal gyrus (ITG), and the right parahippocampal gyrus (PHG), compared to HC. PD patients showed reduced regional homogeneity (ReHo) in the right declive, the right MFG, the left culmen, and the left thalamus, compared to HC. PD patients showed increased ReHo in the right SFG, compared to HC. Additionally, PD patients showed reduced functional connectivity (FC) in the right posterior cingulate (PCG), compared to HC. The present ALE analysis has confirmed functional deficits in motor-, emotion-, and cognition-related regions in PD. Deficits in these regions in rs-fMRI studies could play a role in early diagnosis of PD.

Alternation of the Rich-Club Organization of Individual Brain Metabolic Networks in Parkinson's Disease.

ObjectiveThe diagnosis of Parkinson’s disease (PD) remains challenging. Although 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) has revealed the metabolic abnormalities associated with PD at systemic levels, the underlying rich-club organization of the metabolic connectome in these patients remains largely unknown.Materials and MethodsThe data of 49 PD patients and 49 well-matched healthy controls (HCs) were retrieved and assessed. An individual metabolic connectome based on the standard uptake value (SUV) was built using the Jensen-Shannon Divergence Similarity Estimation (JSSE) method to compare the rich-club properties between PD patients and HC.ResultsOur results showed the rich-club organization of metabolic networks (normalized rich-club coefficients > 1) in the PD and HC group were within a range of thresholds. Further, patients with PD demonstrated lower strength and degree in rich-club connections compared with HCs (strength: HCs = 55.70 ± 8.52, PDs = 52.03 ± 10.49, p = 0.028; degree: HCs = 56.55 ± 8.60, PDs = 52.85 ± 10.62, p = 0.029), but difference between their feeder and local connections was not significant.ConclusionIndividual metabolic networks combined with rich club analysis indicated that PD patients had decreased rich club connections but similar feeder and local connections compared with HCs, indicating rich club connections as a promising marker for early diagnosis of PD.

Diagnostic Value of Speech Acoustic Analysis in Parkinson's Disease

Screening for and identifying patients with Parkinson's disease (PD) at an early stage and forming accurate diagnosis of PD during the course of the progression of the disease are of essential importance but still remain challenging for the clinical diagnosis and treatment of PD. One of the common clinical manifestations of PD is speech impairment, or voice impairment. Thanks to the recent advances in the field of acoustic analysis, a large number of acoustic parameters have been proposed for evaluating speech impairment quantitatively. Early identification and accurate diagnosis of PD was henceforth made possible through the application of speech acoustic analysis. Herein, we summarized the latest research findings on the application of acoustic analysis in PD diagnosis. We reported some acoustic parameters commonly used in the evaluation of voice impairment in PD patients. Then, we presented the diagnostic value of acoustic analysis in developing accurate diagnosis, early screening and differential diagnosis. Furthermore, we discussed the drawbacks and prospects of current studies, intending to enhance understanding of acoustic analysis of PD patients and its potential diagnostic values.

The Pathological Mechanism Between the Intestine and Brain in the Early Stage of Parkinson's Disease.

Parkinson's disease (PD) is the second most common chronic progressive neurodegenerative disease. The main pathological features are progressive degeneration of neurons and abnormal accumulation of α-synuclein. At present, the pathogenesis of PD is not completely clear, and many changes in the intestinal tract may be the early pathogenic factors of PD. These changes affect the central nervous system (CNS) through both nervous and humoral pathways. α-Synuclein deposited in the intestinal nerve migrates upward along the vagus nerve to the brain. Inflammation and immune regulation mediated by intestinal immune cells may be involved, affecting the CNS through local blood circulation. In addition, microorganisms and their metabolites may also affect the progression of PD. Therefore, paying attention to the multiple changes in the intestinal tract may provide new insight for the early diagnosis and treatment of PD.

Development of early diagnosis of Parkinson's disease on animal models based on the intranasal administration of α-methyl-p-tyrosine methyl ester in a gel system.

The fight against neurodegenerative diseases, including Parkinson's disease (PD), is a global challenge of this century. The effectiveness of current PD therapy is limited, since it is diagnosed many years after the onset, following the death of most nigrostriatal dopaminergic neurons regulating motor function. PD treatment could be greatly improved if it was started at an early (preclinical) stage. For this purpose, it is necessary to develop an early diagnosis of PD, which is the goal of our study. We have developed an early diagnosis of PD on animal models using a provocative test by intranasal administration of α-methyl-p-tyrosine methyl ester (αMPTME), a reversible inhibitor of dopamine synthesis. First, we produced the provocative agent, αMPTME in gel, and showed its safety and penetration into the brain bypassing the blood-brain barrier. Then, the optimal dose of αMPTME and time after administration were selected, at which the level of dopamine in the striatum of intact animals decreases, but does not reach the 30% threshold for the appearance of motor disorders in PD patients. Finally, we proved on animal models that intranasal administration of αMPTME can serve as a diagnostic test for preclinical PD. Indeed, intranasal administration of αMPTME to mice in a model of PD at the preclinical stage reversibly reduced the dopamine level in the striatum to the 30% threshold causing short-term motor disorders. Thus, using animal models of PD, we have developed a provocative test for the preclinical diagnosis of PD, a fundamentally new technology in neurology.

Progress prediction of Parkinson's disease based on graph wavelet transform and attention weighted random forest

The progress prediction of Parkinson's disease (PD) is one of the most important issues in early diagnosis of PD. Many researches have been conducted in this field, however, most existing methods focus on the selection of baseline features and regressors to reduce prediction errors. Different from the previous studies, the main goal of this paper is to obtain more effective features by feature transformation of baseline features to improve the prediction performance. Therefore, this paper proposes a prediction model based on graph wavelet transform (GWT) and attention weighted random forest (RF). Firstly, a clustering algorithm is adopted to reduce the prediction error of the model. Next, a multi-scale analysis of the feature vectors by GWT is conducted to yield a frequency feature representation that is more relevant to the target value. Finally, the frequency features are input into the attention weighted RF to predict the severity of PD, allowing the results of decision trees with better predictive performance in the RF to be highlighted while reducing the risk of overfitting. The effectiveness of the method is evaluated on the Parkinson's telemonitoring dataset collected by the University of Oxford. The experimental results show that the mean absolute error and root mean squared error of the proposed method for predicting PD severity (motor- and total-UPDRS) are 1.53, 2.13 and 1.91, 2.70, respectively. Compared with the quoted optimal method, the errors are reduced by 7.27%, 4.05% and 5.45%, 1.10%, respectively. This indicates that the proposed method has better prediction performance.

A Machine Learning Approach to Parkinson's Disease Blood Transcriptomics.

The increased incidence and the significant health burden associated with Parkinson’s disease (PD) have stimulated substantial research efforts towards the identification of effective treatments and diagnostic procedures. Despite technological advancements, a cure is still not available and PD is often diagnosed a long time after onset when irreversible damage has already occurred. Blood transcriptomics represents a potentially disruptive technology for the early diagnosis of PD. We used transcriptome data from the PPMI study, a large cohort study with early PD subjects and age matched controls (HC), to perform the classification of PD vs. HC in around 550 samples. Using a nested feature selection procedure based on Random Forests and XGBoost we reached an AUC of 72% and found 493 candidate genes. We further discussed the importance of the selected genes through a functional analysis based on GOs and KEGG pathways.

Skin Conditions and Movement Disorders: Hiding in Plain Sight.

Skin manifestations are well-recognized non-motor symptoms of Parkinson's disease (PD) and other hypokinetic and hyperkinetic movement disorders. Skin conditions are usually well visible during routine clinical examination and their recognition may play a major role in diagnostic work-up. In this educational review we: (1) briefly outline skin conditions related to Parkinson's disease, including therapy-related skin complications and their management; (2) discuss the role of skin biopsies in early diagnosis of PD and differential diagnosis of parkinsonian syndromes; and focus more on areas which have not been reviewed in the literature before, including (3) skin conditions related to atypical parkinsonism, and (4) skin conditions related to hyperkinetic movement disorders. In case of rare hyperkinetic movement disorders, specific dermatological manifestations, like presence of angiokeratomas, telangiectasias, Mongolian spots, lipomas, ichthyosis, progeroid skin changes and others may point to a very specific group of disorders and help guide further investigations.

Tailoring a luminescent metal − organic framework precise inclusion of Pt-Aptamer nanoparticle for noninvasive monitoring Parkinson's disease

Pathologic α-synuclein (α-Syn) has been implicated as a significant causal agent of Parkinson's disease (PD). Thus, α-Syn is an important target for the development of diagnostic tests and therapeutics for PD. In this work, a fluorescence “turn-on” strategy based on a Tb- luminescent metal−organic framework probe (Tb-MOF@Pt-Aptamer) is developed for the noninvasive monitor of α-Syn via oral delivery. To overcome the harsh gut conditions, acid-stable luminescent Tb-MOF capsules are found to excellently protect aptamer on the surface of Pt nanoparticle via precise pore size of luminescent Tb-MOF. When the α-Syn exists on the gut, the α-Syn can be specially recognized with aptamer in Tb-based luminescent MOF probe. Then Pt-Aptamer/α-Syn complex is released into the gut leading to the notable “turn-on” fluorescence signal to the probe. Additionally, the integrity of luminescent Tb-MOF is retained along the gut and is drained in the feces. Detecting fecal fluorescence intensity may provide a solution to PD detection. The noninvasive fluorescence “turn-on” strategy is a great promising detection progress for the early and potential therapeutics diagnosis of PD.

Blood SSR1: A Possible Biomarker for Early Prediction of Parkinson's Disease.

Parkinson’s disease (PD) is the second most common neurodegenerative disease associated with age. Early diagnosis of PD is key to preventing the loss of dopamine neurons. Peripheral-blood biomarkers have shown their value in recent years because of their easy access and long-term monitoring advantages. However, few peripheral-blood biomarkers have proven useful. This study aims to explore potential peripheral-blood biomarkers for the early diagnosis of PD. Three substantia nigra (SN) transcriptome datasets from the Gene Expression Omnibus (GEO) database were divided into a training cohort and a test cohort. We constructed a protein–protein interaction (PPI) network and a weighted gene co-expression network analysis (WGCNA) network, found their overlapping differentially expressed genes and studied them as the key genes. Analysis of the peripheral-blood transcriptome datasets of PD patients from GEO showed that three key genes were upregulated in PD over healthy participants. Analysis of the relationship between their expression and survival and analysis of their brain expression suggested that these key genes could become biomarkers. Then, animal models were studied to validate the expression of the key genes, and only SSR1 (the signal sequence receptor subunit1) was significantly upregulated in both animal models in peripheral blood. Correlation analysis and logistic regression analysis were used to analyze the correlation between brain dopaminergic neurons and SSR1 expression, and it was found that SSR1 expression was negatively correlated with dopaminergic neuron survival. The upregulation of SSR1 expression in peripheral blood was also found to precede the abnormal behavior of animals. In addition, the application of artificial intelligence technology further showed the value of SSR1 in clinical PD prediction. The three classifiers all showed that SSR1 had high predictability for PD. The classifier with the best prediction accuracy was selected through AUC and MCC to construct a prediction model. In short, this research not only provides potential biomarkers for the early diagnosis of PD but also establishes a possible artificial intelligence model for predicting PD.

Blood-based gene-expression biomarkers identification for the non-invasive diagnosis of Parkinson’s disease using two-layer hybrid feature selection

Parkinson's disease (PD) is one of the most prevalent neurodegenerative diseases. Understanding the molecular mechanism and identifying potential biomarkers of PD promote effective treatments to the patients. Due to less invasiveness and easy accessibility, biomarkers from blood support early detection and diagnosis of PD. This study combined three independent PD microarray gene expression data from blood samples applying the early integration approach. Moderated t-statistics was employed to identify differentially expressed genes (DEGs). Relevant genes were selected using a two-layer embedded wrapper feature selection method with gradient boosting machine (GBM) in the first layer followed by an ensemble of wrappers including Recursive Feature Elimination (RFE), Genetic algorithm (GA) and Bi-directional elimination (Stepwise). All three wrappers were based on logistic regression classifier (LR). The PD-predictability of the generated signature was tested using nine supervised classification models, including eight shallow machine learning and one deep learning. On an independent dataset, GSE72267, Support Vector Machine-Radial (SVMR), and Deep Neural Network (DNN) showed the best performance with AUC 0.821 and 0.82, respectively. Comparison with existing blood-based PD signatures and the biological analysis verified the reliability of the proposed signature.

Design and Implementation of ML Model for Early Diagnosis of Parkinson’s Disease using Gait Data Analysis in IoT Environment

Parkinson’s disease (PD) is the world’s second most neurodegenerative disorder that results in a steady loss of movement. The symptoms in patients occur slowly with the passage of time are and very hard to identify in its initial stage. So, early diagnosis of PD is the foremost need for timely treatment to people. The introduction of smart technologies like the Internet of Things (IoT) and wearable sensors in the healthcare domain offers a smart way of identifying the symptoms of PD patients. In which smart sensors are worn on the patient’s body which continuously monitor the symptoms in patients and track their possible health status. The major objective of this work is to propose a machine learning-based healthcare model that best classifies the subjects into healthy and Parkinson's patients by extracting the most important features. A step regression-based feature selection method is followed to improve the classification of PD. A Shapiro Wilk test is adopted to check the normality of the gait dataset. This model is implemented on three publicly available Parkinson’s datasets collected from three different studies available on Psyionet. All these data sets contain VGRF recordings obtained from eight different sensors placed under each foot. Experimentation is done on the Jupyter notebook by utilizing Python as a programming language. Experimental results revealed that our proposed model with effective pre-processing, feature extraction, and feature selection method achieved the highest accuracy result of 95.54%, 98.80%, and 94.52% respectively when applied to three datasets. Our research inducts knowledge about significant characteristics of a patient suffering from PD and may help to diagnose and cure at an early stage.

Enhancing Parkinson’s Disease Diagnosis Accuracy Through Speech Signal Algorithm Modeling

Parkinson’s disease (PD), one of whose symptoms is dysphonia, is a prevalent neurodegenerative disease. The use of outdated diagnosis techniques, which yield inaccurate and unreliable results, continues to represent an obstacle in early-stage detection and diagnosis for clinical professionals in the medical field. To solve this issue, the study proposes using machine learning and deep learning models to analyze processed speech signals of patients’ voice recordings. Datasets of these processed speech signals were obtained and experimented on by random forest and logistic regression classifiers. Results were highly successful, with 90% accuracy produced by the random forest classifier and 81.5% by the logistic regression classifier. Furthermore, a deep neural network was implemented to investigate if such variation in method could add to the findings. It proved to be effective, as the neural network yielded an accuracy of nearly 92%. Such results suggest that it is possible to accurately diagnose early-stage PD through merely testing patients’ voices. This research calls for a revolutionary diagnostic approach in decision support systems, and is the first step in a market-wide implementation of healthcare software dedicated to the aid of clinicians in early diagnosis of PD.