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

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Towards a Deeper Understanding: Utilizing Machine Learning to Investigate the Association between Obesity and

Isabella Veneziani¹, Alessandro Grimaldi¹, Angela Marra²

¹Department of Nervous System and Behavioural Sciences, Psychology Section, University of Pavia, Piazza Botta, 11, 27100 Pavia, Italy.

Journal of Clinical Medicine

|April 27, 2024

Summary

Artificial intelligence and machine learning show promise in predicting cognitive decline in obesity. These technologies can identify risk factors and biomarkers for personalized prevention and treatment strategies.

Keywords:

BMI artificial intelligence cognitive decline machine learning obesity

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Area of Science:

Neuroscience
Medical Informatics
Public Health

Background:

Obesity is linked to cognitive decline, posing a global health challenge.
Artificial intelligence (AI) and machine learning (ML) are increasingly used in healthcare for data analysis and personalized medicine.
Understanding the intersection of obesity, health consequences, and cognitive function is crucial.

Purpose of the Study:

To systematically review the application of AI and ML techniques in understanding the relationship between obesity and cognitive decline.
To evaluate the potential of AI and ML in identifying risk factors and predictive models for cognitive impairment in obese individuals.

Main Methods:

Systematic literature searches were conducted across multiple major scientific databases (PubMed, Cochrane, Web of Science, Scopus, Embase, PsycInfo).
Eight relevant studies were selected based on predefined inclusion criteria after full-text review.
The review focused on studies employing AI and ML for analyzing obesity-related health consequences and cognitive decline.

Main Results:

AI and ML techniques demonstrate significant utility in assessing risks and predicting cognitive decline in patients with obesity.
AI/ML models successfully identified key risk factors and predictive biomarkers associated with cognitive impairment.
These findings support the development of tailored prevention strategies and personalized treatment plans for obesity-related cognitive issues.

Conclusions:

AI and ML facilitate early detection, prevention, and personalized interventions for obesity-related cognitive decline, potentially reducing healthcare costs and time.
Future research should address ethical considerations, data privacy, and ensure equitable access to these advanced technologies.
The integration of AI and ML offers a promising avenue for managing the complex relationship between obesity and cognitive health.