Predicting Health Decline Related to Aging with Machine Learning: Anticipating Recurrence Trends in the Elderly

The prevalence of age-related disease poses a significant threat in the modern world environment. The early identification of these diseases not only enhances the concern towards the elderly people's health monitoring but also increases the young people's awareness. Machine learning diseases are recognized for identifying and predicting the required output based on the given data. Although the initial study utilizing machine – learning for health monitoring indicated moderate precision, further improvements can be made. Random Forest tends to overfit noisy data, especially when the number of trees in the forest is large and the depth of the trees is not appropriately controlled. Overfitting can lead to poor generalization performance on unseen data, making it less effective in identifying age-related issues in new datasets. to improve the model with the use of machine learning frameworks like Random Forest integrated with the XGBoost algorithm with in-built crossvalidation. The RF can provide stability, while Gradient Boosting can improve the accuracy. The algorithm uses the tree-pruning approach to increase the performance.