Optimizing Successful Aging Prediction with Ensemble Machine Learning Techniques
Authors: V. Raja Venkat Ram, P. Pranay, M. Raghavender Sharma
DOI: 10.87349/JBUPT/281012
Page No: 8-16
Abstract
Individual differences in cognitive and successful again are observed across individuals, and these differences are influenced by a reserve or defense mechanism that strengthens the brain’s resistance to age-related dam-age. According to the Neurocognitive Hypothesis in cognitive neuroscience, this reserve develops through intellectually demanding activities and lifelong experiences. The statistical and machine learning modeling presented here explains how the neurocognitive reserve impacts changes in brain architecture, neurons, and neural activation patterns due to age and individual-related factors. The modeling is based on behavioural and neuroimaging findings, with preliminary results from structural and functional neuroimaging supporting the idea that neurocognitive reserve functions as a neural resource, reducing the impact of cognitive decline caused by aging, neurological, and psychological diseases. The paper emphasizes that neurocognitive reserve offers a dynamic view of resilience, demonstrating the ability to adjust to brain illness and damage as predicted by statistical models. Although the processes outside the model are not fully under-stood, the study advocates that predictive modeling can aid future research in identifying the elements that support neurocognitive reserve’s positive impacts in delaying cognitive decline and fostering psychological resilience in old age.
