Advanced predictive analytics with synthetic data: A comprehensive machine learning approach for predicting chronic and lifestyle diseases

Abstract

With advancements in technology, artificial intelligence has become a breakthrough in the biomedical field and healthcare management. Chronic and lifestyle diseases are difficult to predict, primarily because of the multifactorial environment in which various risks and health consequences interact. The integration of synthetic data with machine learning techniques constitutes a revolutionary approach to mitigate these issues, enabling the enlargement of data variability and the elimination of constraints associated with real-world datasets. To achieve the objectives of this study, the following review was conducted to understand the prognosis of chronic and lifestyle diseases using synthetic datasets and by implementing different machine learning techniques. An analysis of the publication metadata of sources exclusively provided insights into research trends. A focused evaluation of recent studies uncovered four key analytical approaches: data-mining techniques that may be employed, including data mining, clustering, analysis of social networks, and risk assessment. This study reveals how machine learning using synthetic data solves the problem of data scarcity and provides valuable insights into healthcare risk factors. The present research provides a framework for enhancing predictive analytical science for better prevention, diagnosis, and management of chronic and lifestyle diseases, with an accuracy of 95% for random forest and 94% for gradient boosting.