AI-Driven Predictive Analytics for Early Diagnosis and Healthcare Cost Reduction

Abstract

The increasing healthcare costs and the escalating number of chronic and acute diseases require novel scalable solutions that will allow early diagnosis and maximum utilization of resources. Predictive analytics based on Artificial Intelligence (AI) is becoming a potent tool to determine the risk of diseases in their early stages by utilizing large and heterogenous clinical data. This paper will introduce an AI-based predictive analytics system aimed at facilitating the process of early disease detection, smart clinical decision-making, and healthcare cost savings. The suggested framework combines data preprocessing, feature engineering and supervised machine learning models to find clinically significant patterns in electronic health records and other medical data. The standard clinical measures are used to assess the model performance, which will guarantee accuracy, robustness, and reproducibility. One of the contributions of this work is that it focuses on cost-effectiveness as well as actual deplorability. The predictive models are optimized to perform highly at diagnostic accuracy and low computational and operational costs so that they can be used in a wide variety of healthcare environments without the need to rely on costly infrastructure. The framework can help streamline the screening and preventive interventions and reduce unwarranted diagnostic tests, late-stage treatment, and unnecessary hospitalization by enabling early risk detection and patient stratification. The automated decision support features are also beneficial in lowering clinical workflow efficiency because there is less manual review and administrative workload. All in all, this study shows that predictive analytics based on AI can enhance diagnoses timeliness, increase healthcare efficiency, and lead to cost reductions that will result in sustainable and economically feasible healthcare systems.

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