Survey on Comparison of Brain Age with Human Age

Machine learning (ML) models play a significant role in the brain age prediction structure however the analysis of how regression models affect precision is still in its early stages of investigation.By comparing the predicted brain age with the chronological age we determine the significance of specific algorithms in precisely calculating brain age in our proposed system.The models outcomes demonstrate the capabilities of antiquated machine learning algorithms particularly those found in neural networks and regression algorithms.The accuracy of brain age assessments in clinical stages should increase with the use of these methods.By improving brain age estimation accuracy our method will help with more neurological disease evaluations.Regression algorithms and neural network capabilities are employed to achieve this.This study provides information to aid in the development of clinical applications and identifies the major gaps in the assessment of machine learning algorithms for estimating brain age.By providing more precise and effective diagnoses the use of sophisticated algorithms not only improves accuracy but also transforms neurology.