Developing a Model for Parkinson’s Disease Detection Using Machine Learning Algorithms

Parkinson’s disease(PD)is a chronic neurological condition that progresses over time.People start to have trouble speaking,writing,walking,or performing other basic skills as dopamine-generating neurons in some brain regions are injured or die.The patient’s symptoms become more severe due to the worsening of their signs over time.In this study,we applied state-of-the-art machine learning algorithms to diagnose Parkinson’s disease and identify related risk factors.The research worked on the publicly available dataset on PD,and the dataset consists of a set of significant characteristics of PD.We aim to apply soft computing techniques and provide an effective solution for medical professionals to diagnose PD accurately.This research methodology involves developing a model using a machine learning algorithm.In the model selection,eight different machine learning techniques were adopted:Namely,Random Forest(RF),Decision Tree(DT),Support Vector Machine(SVM),Naïve Bayes(NB),Light Gradient Boosting Machine(LightGBM),K-Nearest Neighbours(KNN),Extreme Gradient Boosting(XGBoost),and Logistic Regression(LR).Subsequently,the concentrated models were validated through 10-fold Cross-Validation and Receiver Operating Characteristic(ROC)—Area Under the Curve(AUC).In addition,GridSearchCV was utilised to measure each algorithm’s best parameter;eventually,the models were trained through the hyperparameter tuning approach.With 98%accuracy,LightGBM had the highest accuracy in this study.RF,KNN,and SVM came in second with 96%accuracy.Furthermore,the performance scores of NB and LR were recorded to be 76%and 83%,respectively.It is to be mentioned that after applying 10-fold cross-validation,the average performance score of LightGBM accounted for 93%.At the same time,the percentage of ROC-AUC appeared at 0.92,which indicates that this LightGBM model reached a satisfactory level.Finally,we extracted meaningful insights and figured out potential gaps on top of PD.By extracting meaningful insights and identifying potential gaps,our study contributes to the significance and impact of PD research.The application of advanced machine learning algorithms holds promise in accurately diagnosing PD and shedding light on crucial aspects of the disease.This research has the potential to enhance the understanding and management of PD,ultimately improving the lives of individuals affected by this condition.

Pathogenic genes associated with Parkinson’s disease:molecular mechanism overview

Parkinson’s disease(PD)is a common neurodegenerative disease in the elderly,accounting for more than 1%of the population aged 65 years.Monogenic inheritance is relatively rare in PD,accounting for approximately 5%to 10%of PD patients,and there is a growing body of evidence suggesting that multiple genetic risk factors play a significant role in the pathogenesis of PD.Several groups have identified and reported a number of genes carrying mutations associated with affected family members.Mutated genes associated with PD are also candidates for idiopathic PD,and these genes may also carry other mutation sites that increase risk.When multiple genetic risk factors are combined,the risk of PD is increased to a greater extent,and to unravel the pathogenic pathways that lead to different forms of PD.This review focuses on the association of PD genes,such as Parkinson Disease 1-24(PARK1-24),glucosylceramidase(GBA),GTP cyclohydrolase 1(GCH1),fibroblast growth factor 20(FGF20),nuclear receptor-related factor 1(NURR1),NUS1 dehydrodolichyl diphosphate synthase subunit(NUS1),diacylglycerol Lipase Beta(DAGLB),transmembrane protein(TMEM),ubiquinol-cytochrome c reductase core protein 1(UQCRC1),glycoprotein non-metastatic melanoma protein B protein(GPNMB),dynactin 1(DCTN1),LDL receptor related protein 10(LRP10),monoamine oxidase(MAO),ataxin 2(ATXN2),microtubule associated protein tau(MAPT),pantothenate kinase 2(PANK2),spastic parapplegia type 11(SPG11),polymer gamma(POLG),TATA-box binding protein associated factor 1(TAF1),dual specificity tyrosine phosphorylation regulated kinase 1A(Dyrk1a),and crystallin alpha A(CRYAA),with the pathogenesis of PD.We introduce what is currently known about the molecular genetics of PD to help explain the molecular mechanisms leading to the neurodegenerative disease.

Effects of a Cocktail Supplement of Ginkgo Biloba and Acai Extract on Cognitive Symptoms of Parkinson’s Disease

Parkinson’s Disease (PD) is a neurodegenerative disorder characterized by motor and non-motor symptoms, including cognitive impairment. Current treatments often involve synthetic drugs with significant side effects and potential for dependency. This study investigates the effects of a natural supplement combination of Ginkgo Biloba and Acai Extract on cognitive symptoms in a 77-year-old male with PD. The participant underwent a three-month supplementation regimen, with cognitive function assessed using the Montreal Cognitive Assessment (MoCA) test before and after the intervention. The results indicated an improvement in cognitive scores, suggesting that the combination of Ginkgo Biloba and Acai Extract may offer a promising alternative or adjunct to conventional PD treatments. This study highlights the potential of natural supplements in managing PD symptoms and calls for further research with larger sample sizes to confirm these findings. Human data was performed in accordance with the Declaration of Helsinki by the Roxbury District IRB Board (IRB Number: IRB00011767).

Pro-inflammatory cytokines levels in tears and dry eye disease in Parkinson’s disease

Background:Neuroinflammation is an essential event in Parkinson’s disease(PD).Identifying affordable and less invasive biomarkers to make an early diagnosis and monitor therapeutic strategies should be a priority among researchers.The study’s objective was to measure tear levels of cytokines in subjects with PD and their association with motor features and the presence of dry eye symptoms.Methods:A total of 16 subjects with PD and 16 age-and sex-matched controls were included.Movement Disorders Society-Unified Parkinson’s Disease Rating Scale(MDS-UPDRS),Hoehn and Yahr(HY)stage scale,Montreal Cognitive Assessment(MoCA),tear break-up time(TBUT),blink rate(BR),Dry Eye Questionnaire 5(DEQ-5)were examined,and pro-inflammatory cytokines[interleukin(IL)-1β,IL-6,IL-8,IL-10,IL-12p70 and tumor necrosis factor-alpha(TNF-α)]were quantified in tears using the BD Cytometric Bead Array Human Inflammatory Cytokine Kit.Results:Higher tear TNF-αwere quantified in PD compared to controls(2.94±3.95 vs.0.33±0.49 pg/mL,P=0.008).According to DEQ-5,50.0%(n=8)of PD subjects and 12.5%(n=2)controls had dry eye disease(DED).No differences were found in cytokines concentrations between PD patients with DED compared to those without DED.IL-8 was associated with the HY stage,TBUT,DEQ-5,and a better MoCA score.A higher BR correlated moderately with a lower HY stage(r=−0.645,P=0.007),and DED patients have lower BR in PD(12.14±2.54 vs.9.0±2.06 blinks/minute,P=0.031).Conclusions:PD patients have higher levels of TNF-αin tears than age-and sex-matched HC.IL-8 in tears may be both involved in the severity of the disease and in the development of DED in PD.In addition,our findings suggest that as HY stage increases,indicating a more advanced stage,BR decreases,indicating greater motor impairment.Conversely,the presence of DED is associated with higher levels of bradykinesia in PD patients,suggesting a potential relationship between DED and motor impairment severity.

Classification of Short Time Series in Early Parkinson’s Disease With Deep Learning of Fuzzy Recurrence Plots

There are many techniques using sensors and wearable devices for detecting and monitoring patients with Parkinson’s disease(PD).A recent development is the utilization of human interaction with computer keyboards for analyzing and identifying motor signs in the early stages of the disease.Current designs for classification of time series of computer-key hold durations recorded from healthy control and PD subjects require the time series of length to be considerably long.With an attempt to avoid discomfort to participants in performing long physical tasks for data recording,this paper introduces the use of fuzzy recurrence plots of very short time series as input data for the machine training and classification with long short-term memory(LSTM)neural networks.Being an original approach that is able to both significantly increase the feature dimensions and provides the property of deterministic dynamical systems of very short time series for information processing carried out by an LSTM layer architecture,fuzzy recurrence plots provide promising results and outperform the direct input of the time series for the classification of healthy control and early PD subjects.

Bioinformatics Analysis Raises Candidate Genes in Blood for Early Screening of Parkinson's Disease

Parkinson＇s disease （PD） is a typical degenerative disease, which is characterized by the most obvious symptoms of movement dysfunction, including shaking, rigidity, slowness of movement and difficulty in walking and gait. This disease can not be clearly identified through laboratory tests at present, thus application of high-throughput technique in studying the expression profiles of PD helps to find the genetic markers for its early diagnosis. Studies on expression profiles of neurodegenerative diseases have revealed the novel genes and pathways involved in the progress of illness. In this study, the expression profiles of PD in blood were compared, showing that 181 differentially expressed genes （DEG） exhibit a similar expression trend both in patients and in normal controls.

Magnetic resonance imaging markers for early diagnosis of Parkinson's disease

Parkinson＇s disease （PD） is a neurodegenerative disorder characterized by selective and progressive degeneration, as well as loss of dopaminergic neurons in the substantia nigra. In PD, approximately 60-70% of nigrostriatal neurons are degenerated and 80% of content of the striatal dopamine is reduced before the diagnosis can be established according to widely accepted clinical diagnostic criteria. This condition describes a stage of disease called ＂prodromal＂, where non-motor symptoms, such as olfactory dysfunction, constipation, rapid eye movement behaviour disorder, depression, precede motor sign of PD. Detection of prodromal phase of PD is becoming an important goal for determining the prognosis and choosing a suitable treatment strategy. In this review, we present some non-invasive instrumental approaches that could be useful to identify patients in the prodromal phase of PD or in an early clinical phase, when the first motor symptoms begin to be apparent. Conventional magnetic resonance imaging （MRI） and advanced MRI techniques, such as magnetic resonance spectroscopy imaging, diffusion-weighted and diffusion tensor imaging and functional MRI, are useful to differentiate early PD with initial motor symptoms from atypical parkinsonian disorders, thus, making easier early diagnosis. Functional MRI and diffusion tensor imaging techniques can show abnormalities in the olfactory system in prodromal PD.

Parkin and <i>LRRK2</i>/Dardarin Mutations in Early Onset Parkinson’s Disease in the Basque Country (Spain)

We have performed a complete screening of the Parkin gene (PRKN2) and looked for p.Gly2019Ser (G2019S) and p.Arg1441Gly (R1441G) LRRK2/dardarin gene mutations in twenty seven patients with Parkinson’s disease (PD) with an age at onset younger than 50 years (EOPD), living in Gipuzkoa (Basque Country, Spain). Thirteen of them (48%) were PRKN2 mutation carriers. The c.255-256DelA mutation was the most frequent, followed by a deletion involving exons 3 and 4. A deletion involving exons 3 and 12 of the PRKN2 gene and R1441G LRRK2 mutation was found together in one PD patient. Four out of fourteen PRKN2 negative patients carried the p.G2019S mutation. Both PRKN2 mutation carriers and non-carriers presented frequently with family history (10 PRKN2 mutation carriers and 8 PRKN2 non-carriers);in fact, five patients without a known gene mutation had a first degree relative affected, suggesting another monogenic disease. PRKN2 carriers presented with a younger age at onset (36.7 vs. 41.7) and more benign disease progression. Indeed, those PD patients younger than forty who initially presented with unilateral tremor became shortly bilateral. Relatively, symmetric parkinsonism and slow disease progression carried more frequently PRKN2 mutations than patients with unilateral akinetic rigid parkinsonism and age at onset later than 40 years. As expected in a recessive disease, PRKN2 patients present more often with affected siblings and unaffected patients. The G2019S LRRK2 mutation, less prevalent than R1441G in our area, may be also a frequent cause of PD in EOPD (4 patients).

Pharmacotherapeutics and molecular docking studies of alphasynuclein modulators as promising therapeutics for Parkinson’s disease

Parkinson’s disease(PD)is an age-related neurodegenerative ailment that affects dopamine-producing neurons in a specific area of the brain called the substantia nigra of the ventral midbrain.It is clinically characterized by movement disorder and marked with unusual synaptic protein alpha-synuclein accumulation in the brain.To date,only a few Food and Drug Administration(FDA)approved drugs are available on the market for the treatment of PD.Nonetheless,these drugs show parasympathomimetic related adverse events and remarkably higher toxicity;hence,it is important to find more efficacious molecules to treat PD.In our study,We chosen 22 natural compounds as inhibitors that potentially block the alpha-synuclein clump-the pathological hallmark of PD-and provide new avenues for its treatment.Most of these molecules exhibited good pharmacokinetic behaviors,making them decisively favorable drug candidates to cure PD.Molecular docking studies were performed to investigate the binding interactions between natural compounds and alpha-synuclein as anti-Parkinson drug targets.Among the examined compounds,curcumin and piperine emerged as promising phytochemicals with the highest binding affinity,key residual stable bindings and showed a good inhibitory features.Thus,the present study indicates that curcumin and piperine hold the potential to be developed as treatment options against PD.Experimental validations are needed for insights into their mechanism of action and potential clinical application.

A review of computational modeling and deep brain stimulation:applications to Parkinson’s disease

Biophysical computational models are complementary to experiments and theories,providing powerful tools for the study of neurological diseases.The focus of this review is the dynamic modeling and control strategies of Parkinson’s disease(PD).In previous studies,the development of parkinsonian network dynamics modeling has made great progress.Modeling mainly focuses on the cortex-thalamus-basal ganglia(CTBG)circuit and its sub-circuits,which helps to explore the dynamic behavior of the parkinsonian network,such as synchronization.Deep brain stimulation(DBS)is an effective strategy for the treatment of PD.At present,many studies are based on the side effects of the DBS.However,the translation from modeling results to clinical disease mitigation therapy still faces huge challenges.Here,we introduce the progress of DBS improvement.Its specific purpose is to develop novel DBS treatment methods,optimize the treatment effect of DBS for each patient,and focus on the study in closed-loop DBS.Our goal is to review the inspiration and insights gained by combining the system theory with these computational models to analyze neurodynamics and optimize DBS treatment.

Efficacy of cognitive rehabilitation in Parkinson's disease

Cognitive rehabilitation is a potential and promising treatment for cognitive impairment in Parkinson＇s disease （PD） that has shown efficacy in diverse studies. In addition, some few studies have found brain changes after cognitive rehabilitation in PD, which supports the existence of brain plasticity associated to cognitive training in a degen- erative disease.

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.

Function and mechanism of traditional Chinese medicine in the prevention and treatment of Parkinson＇s disease

Parkinson’s disease （PD） was frst discovered 200 years ago. The current gold standard of clinical treatment is still mainly levodopa replacement therapy. Traditional Chinese medicine is the foundation of traditional medicine in China. Chinese herbs and acu-puncture both exhibit remarkable effcacy in the treatment of PD. Clinical studies on the treatment of PD using Chinese herbs have confrmed that the combined use of Chinese herbs and the levodopa formulation can signifcantly increase the treatment effect and reduce toxic side effects. Basic studies further confrmed that various Chinese herbs and their monomeric substances can protect dopaminergic neurons in PD models. The major mechanisms include anti-infammation, anti-oxidant, anti-apoptosis, neuroprotection, mitochondrial function protection, and regulation of gut microbiota. The function of acupuncture in the treatment of PD has also gradually received extensive attention in China and other countries. Acupuncture not only has peculiar advantages in the improvement of symptoms of PD patients, but also can attenuate adverse drug reactions, delay disease progression, and increase the quality of life of patients. Basic studies further confrmed that acupuncture can improve many motor symptoms in animal models of PD and has cumulative effects and follow-up effects. The major mechanisms include dopamin-ergic neuron protection functions, anti-infammation and anti-oxidant effects, and the regulation of related neuro-transmitters and neural circuits. The clinical application of acupuncture and Chinese herbs still requires strict ran-domized, double-blind, controlled design, multi-centre and large-sample size evidence-based clinical studies and follow-up observations of long-term effcacy to support the effect. In addition, the multi-target and multi-pathway therapeutic mechanisms need further studies.

Parkinson’s Disease Detection Using Biogeography-Based Optimization

In recent years,Parkinson’s Disease(PD)as a progressive syndrome of the nervous system has become highly prevalent worldwide.In this study,a novel hybrid technique established by integrating a Multi-layer Perceptron Neural Network(MLP)with the Biogeography-based Optimization(BBO)to classify PD based on a series of biomedical voice measurements.BBO is employed to determine the optimal MLP parameters and boost prediction accuracy.The inputs comprised of 22 biomedical voice measurements.The proposed approach detects two PD statuses:0-disease status and 1-good control status.The performance of proposed methods compared with PSO,GA,ACO and ES method.The outcomes affirm that the MLP-BBO model exhibits higher precision and suitability for PD detection.The proposed diagnosis system as a type of speech algorithm detects early Parkinson’s symptoms,and consequently,it served as a promising new robust tool with excellent PD diagnosis performance.

Genetic association of Parkinson's disease

Totally three articles regarding associations of Nurrl gene mutations, LRRK2 gene polymorphism sites $1647T and R1398H, and polymorphism of PARK2 gene mutations with Han Chinese patients with Parkinson＇s disease were published in Neural Regeneration Research. We hope that our readers find these papers useful to their research.

Non-motor signs and symptoms in Parkinson’s disease

Motor symptoms are cardinal clinical features of Parkinson’s disease (PD). Progress in drug therapy and rehabilitation has been presenting beneficial effect for motor symptoms. However, non-motor symptoms and signs in PD have been accumulated growing attentions and its amelioration may also give beneficial effect for PD patients’ and their care givers’ quality of life. In this mini-review, I overviewed non-motor symptoms and signs in PD.

Alpha-synuclein in body fluids as a diagnostic biomarker for Parkinson’s disease

Parkinson’s disease(PD)is a common neurodegenerative disease,characterized clinically by both motor and non-motor symptoms.Pathologically,PD is hallmarked by the loss of dopaminergic neurons in the substantia nigra(SN)and the formation ofα-synuclein(α-syn)containing inclusion bodies(Lewy pathology)in the surviving neurons.Diagnosis of PD is still based on clinical features.However,owing to the complexity,heterogeneity,and overlapping of its symptoms with other Parkinsonian disorders,correct diagnosis of PD remains a challenge,especially in the early stages.Therefore,there is an urgent need for biomarkers that can help correctly diagnose PD,differentiate PD from other Parkinsonian disorders,monitor the progression of the disease,and evaluate the therapeutic efficacy.Various molecules have been investigated for their utility in diagnosing PD,among whichα-syn is the most extensively investigated one due to its close implication in the etiology and pathogenesis of PD and related diseases.During the past decade,various species ofα-syn,including total,oligomeric,and phosphorylatedα-syn in various tissues,have been investigated for their utility as a potential biomarker for PD diagnosis and differential diagnosis.Various forms ofα-syn in body fluids,including cerebrospinal fluid(CSF),blood plasma,and saliva,are among the ones that are extensively investigated,since the body fluids are relatively accessible compared to the peripheral tissues.The aim of this review is to summarize the progress of studies on the utility ofα-syn in body fluid as a biomarker for PD diagnosis and differential diagnosis.

Sleep Disturbance in Parkinson’s Disease Varies with Age of Onset and Family History

Introduction: Parkinson’s disease (PD) is a progressive neurodegenerative disease more common in those over the age of 60. PD is classically characterized by motor features, although patients may also experience non-motor symptoms. Sleep disturbances, such as rapid eye movement (REM) behavior disorder (RBD), are common in patients with PD and may precede onset of PD. Methods: Data was collected on patients with PD (358 subjects)in a movement disorders clinic at a safety net hospital. In this retrospective database analysis, the association of PD complications with age of onset was evaluated using chi-square tests and logistic regression. Results: Of the PD complications analyzed, there was a significant difference in sleep disturbances by age. Among the 358 PD patients, 120 individuals (33.5%) had information regarding the presence or absence of sleep disturbances. There was a significant difference between the early (onset < 50) and later onset (≥50) groups (p = 0.03) with the odds of having a sleep disorder for the early group 1.6 times that of the late group. Those subjects with siblings who also had PD had 2.0 times the odds of having a sleep disorder compared those without (p = 0.02). Conclusion: Non-motor symptoms such as sleep disorders are a useful predictor of early onset PD. Genetic components of PD impact both motor and non-motor aspects of the disease.

Pathogenic roles of alpha-synuclein in Parkinson’s disease and multiple system atrophy

Alpha-synucleinopathies(α-synucleinopathies)are a diverse group of neurodegenerative diseases comprising Parkinson’s disease(PD),dementia with Lewy bodies(DLB),and multiple system atrophy(MSA).Although in all these diseases there exist abnormal accumulation of alpha-synuclein(α-syn)aggregates in nerve tissues,the pathological lesions formed byα-syn aggregates and their cellular locations are quite different.In PD and DLB,the hallmark pathological lesions are Lewy bodies(LBs)and Lewy neurites(LNs),which are localized in the neuronal somata and processes.In MSA,the characteristic pathologic structures are glial cytoplasmic inclusions,which are deposited in the cytoplasm of oligodendrocytes.The fact that PD and MSA have distinct pathologicalα-syn lesions suggest that different mechanisms play a role in the pathogenesis of the two diseases.In this review article,we compare the clinical manifestations and pathological features of PD and MSA,the two common synucleinopathies,and discuss the potential mechanisms for the formation ofα-syn aggregates and their pathologic roles in PD and MSA.

Parkinson’s disease: how should we consider the selectivity and progressivity of its neuropathology?

Parkinson’s disease(PD)is a common age-related neurodegenerative disease characterized by movement disorders.The hallmark pathological lesions of PD are the formation of Lewy pathology in selected populations of neurons throughout the nervous system.Braak and his colleagues created a staging system for PD describing the connection between Lewy pathology and disease severity.They proposed that Lewy pathology might be initially triggered by exogenous pathogens targeting the enteric or olfactory nervous system,then spread in a prion-like propagation manner from the peripheral nerves to the lower brainstem and midbrain,before finally reaching higher cortical structures,causing a sequential occurrence of the non-motor and motor symptoms,depending on the lesioned neurons.However,emerging evidence also supports a functional threshold hypothesis proposed by Engelender and Isacson in which Lewy pathology may occur parallelly in the central and peripheral nervous systems and the symptoms only begin when the functional reserve of the affected neurons(and their connecting brain regions)is unable to allow for network compensation.Consequently,early symptoms of PD reflect the loss of function in the least compensated systems,such as the enteric and olfactory nervous systems,rather than the spread of Lewy pathology from the peripheral to the central nervous systems.The current review article provides a comprehensive overview of the evidence supporting a merged mechanism that the neurodegeneration in PD happens to those neurons that are not only intrinsically vulnerable but also affected by the spread of Lewy pathology.