Cognitive Brain Age Estimation

Authors

  • Surya A UG – Computer Science and Engineering, Kamaraj College of Engineering and Technology, Virudhunagar, Tamil Nadu, India. Author
  • Kavya Sri Vaipava S UG – Computer Science and Engineering, Kamaraj College of Engineering and Technology, Virudhunagar, Tamil Nadu, India. Author
  • Ashika Deulin J UG – Computer Science and Engineering, Kamaraj College of Engineering and Technology, Virudhunagar, Tamil Nadu, India. Author
  • Janani S Assistant Professor, Computer Science and Engineering, Kamaraj College of Engineering and Technology, Virudhunagar, Tamil Nadu, India. Author

DOI:

https://doi.org/10.47392/IRJAEH.2025.0089

Keywords:

Non-Invasive Assessment, Machine Learning, Healthcare Innovation, Cognitive Brain Age, Behavioral Patterns

Abstract

Cognitive brain age estimation combines computational techniques with health sciences to assess cognitive health. Traditional methods like neuroimaging and clinical evaluations are costly and not scalable. This research proposes a machine learning-based system for cognitive age estimation using non-invasive data, including speech patterns, behavioral metrics, and lifestyle factors. The system follows a modular architecture with data collection, preprocessing, feature extraction, and predictive modelling. By analyzing behavioral logs, speech characteristics, and lifestyle metrics, it generates real-time cognitive age estimates. This scalable and cost-effective approach, free from neuroimaging, enables deployment in healthcare settings and wearable devices. It also supports large-scale applications, such as public health monitoring and aging studies, enhancing accessibility, early detection, and personalized interventions in cognitive health.

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Published

2025-03-22

How to Cite

Cognitive Brain Age Estimation. (2025). International Research Journal on Advanced Engineering Hub (IRJAEH), 3(03), 648-652. https://doi.org/10.47392/IRJAEH.2025.0089

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