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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
EXPLORING CLINICAL VARIATIONS AND CO-MORBID TRENDS IN PD-MCI GROUPS
K Ganapathy1, B Kumar2, Sh Shekhawat3
11Department of Biotechnology, School of Sciences, JAIN (Deemed-to-be University), Karnataka, India.
Parkinson's Disease with Mild Cognitive Impairment (PD-MCI) significantly impacts visuospatial and executive functions. Specific PD-MCI groups show distinct neurological and motor deficits, highlighting the condition's complexity.
Area of Science:
- Neurology
- Cognitive Science
Background:
- Parkinson's Disease with Mild Cognitive Impairment (PD-MCI) presents with both motor and cognitive deficits.
- Understanding the specific cognitive and neurological variations within PD-MCI is crucial for targeted interventions.
Purpose of the Study:
- To investigate the distinct medical indicators and associated tendencies among different PD-MCI patient groups.
- To identify the most affected cognitive domains and neurological characteristics in PD-MCI.
Main Methods:
- Statistical analysis using SPSS was performed on data from 132 PD-MCI patients.
- Cognitive and neurological assessments were conducted to identify group variations.
Main Results:
- Visuospatial ability and attentional/executive functions were the most impaired cognitive areas across all PD-MCI variants.
- Multi-domain amnesia (Non-Memory) PD-MCI patients exhibited severe issues with posture and walking.
Conclusions:
- PD-MCI is a complex condition with varied cognitive and neurological profiles.
- The interplay between cognitive and physical symptoms in PD-MCI is influenced by specific cognitive domains, brain regions, and overall impairment levels.
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