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Related Concept Videos

Parkinson Disease l: Introduction01:24

Parkinson Disease l: Introduction

Parkinson’s disease is a chronic, progressive neurodegenerative disorder that primarily affects movement. It is characterized by motor symptoms such as resting tremors, muscle rigidity, bradykinesia (slowness of movement), and postural instability. Patients may notice hand tremors at rest, stiffness during movement, or a shuffling gait. In addition to motor features, non-motor symptoms include sleep disturbances, mood and behavioral changes, constipation, and cognitive impairment, all of which...
Parkinson's Disease: Treatment01:24

Parkinson's Disease: Treatment

Neurodegenerative disorders, such as Parkinson's Disease (PD), involve the gradual and irreversible destruction of neurons in particular brain areas. These disorders exhibit standard features like proteinopathies, selective vulnerability of some neurons, and an interaction of intrinsic properties, genetics, and environmental influences in neural injury.
Parkinson's Disease is primarily a result of the loss of dopaminergic neurons in the substantia nigra pars compacta. The cornerstone of its...
Parkinson Disease ll: Pathophysiology01:24

Parkinson Disease ll: Pathophysiology

Parkinson disease (PD) is a progressive neurodegenerative disorder primarily affecting movement, with additional non-motor features. Its pathophysiology involves complex interactions among genetic susceptibility, environmental exposures, and cellular dysfunction, including dopaminergic neuron loss, protein aggregation, and mitochondrial impairment.Selective NeurodegenerationA key feature is the degeneration of dopaminergic neurons in the substantia nigra pars compacta, leading to reduced...
Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a survival tree begins...
Parkinson's Disease: Overview01:15

Parkinson's Disease: Overview

Neurodegenerative disorders are progressive diseases that cause irreversible damage and loss to neurons in specific brain areas. Examples of these disorders include Parkinson's disease, Alzheimer's disease, Multiple Sclerosis (MS), and Amyotrophic Lateral Sclerosis (ALS). These disorders share characteristics such as proteinopathies, selective neuronal vulnerability, and a complex interplay between genetic and environmental factors. The primary therapeutic goal for these conditions is to...

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Related Experiment Video

Updated: May 14, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Predication of Parkinson's disease using data mining methods: a comparative analysis of tree, statistical, and

Geeta Yadav1, Yugal Kumar, Gadadhar Sahoo

  • 1Department of Pharmaceutical Sciences, Birla Institute of Technology, Mesra, Ranchi, Jharkhand, India.

Indian Journal of Medical Sciences
|February 9, 2013
PubMed
Summary

This study explored speech articulation difficulties to predict Parkinson's disease (PD) using three data mining models. The research aimed to identify the most accurate method for early PD detection based on speech patterns.

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Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
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Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

Related Experiment Videos

Last Updated: May 14, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
10:28

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

Area of Science:

  • Neurology
  • Computer Science
  • Data Mining

Background:

  • Early prediction of Parkinson's disease (PD) is challenging due to symptom onset in middle to late age.
  • Speech articulation difficulties are a key symptom in individuals affected by PD.
  • Existing diagnostic methods often identify PD after significant neurological progression.

Purpose of the Study:

  • To develop and evaluate predictive models for early Parkinson's disease detection.
  • To focus on speech articulation as a primary indicator for PD identification.
  • To compare the efficacy of different data mining techniques in classifying PD patients.

Main Methods:

  • Utilized three distinct data mining classification methods: tree classifiers, statistical classifiers, and support vector machines.
  • Focused analysis on speech articulation difficulty data from individuals with and without PD.
  • Assessed classifier performance using accuracy, sensitivity, and specificity metrics.

Main Results:

  • The study compared the performance of tree, statistical, and support vector machine classifiers in identifying PD based on speech data.
  • Performance metrics including accuracy, sensitivity, and specificity were used to evaluate each model.
  • Identified the most accurate data mining model for early detection of Parkinson's disease.

Conclusions:

  • Data mining techniques show promise for early Parkinson's disease detection using speech analysis.
  • The comparative analysis highlights the strengths of specific classifiers in identifying PD-related speech changes.
  • Accurate early identification through speech patterns can potentially improve patient outcomes and treatment strategies.