Golden age prediction using CNN algorithm

Resumo

Forecasting when a person will go into their golden age, or even when they will be at their most productive, healthy, or financially secure, is a highly analytical task that requires the use of data. Still, recent developments in artificial intelligence (AI) give probable support due to their ability to learn higher-level features of raw data automatically, convolutional neural networks (CNNs). The current researcher proposes a CNN-based model, which implements probes on multi-dimensional datasets that may include physiological, behavioral, and socioeconomic indicators into their health records, lifestyle choices, work history, and financial information. It further entails the technical framework that comprises preprocessing, extraction of features, model training, and test operations, and all these operations provide effective and generalized predictions. Empirical evidence indicates how CNNs perform better than traditional machine-learning algorithms, which are as follows: decision trees, SVMs, and ANNs, since they would bring to light any complex and non-linear relationship involving the data, with cardiovascular health, career stability, and mental well-being being the first predictors of a golden age. Therefore, the framework has notable uses in career planning, health, and financial planning through the provision of customized information on informed life choices and as a directional guide to policies that policymakers who aim at creating focused welfare programs. Despite its benefits, the study does not overlook an information scarcity, privacy issues, and some kind of bias, which also implies the need for further research into self-supervised and federated learning, ethically acceptable AI, and its better interpretability to make the prediction of the golden age using CNN more feasible and socially acceptable.