Predictive Forecasting for Early Risk Detection in Smart Healthcare Systems using AI

Abstract

The rapid growth of artificial intelligence (AI) has transformed modern healthcare by enabling predictive analysis and early detection of medical risks. Traditional health monitoring systems primarily offer reactive alerts based on threshold violations, often failing to identify hidden patterns in physiological signals such as ECG, blood pressure, glucose levels, and kidney function indicators. To address these limitations, AI-driven predictive forecasting models— particularly LSTM, GRU, CNN, and Transformer-based architectures—have emerged as powerful tools for analyzing time-series health data and forecasting potential risks before they occur. This survey paper reviews state-of-the-art research, existing methodologies, datasets, and machine learning techniques used for early health risk prediction. The paper highlights trends, compares model performance, identifies current research gaps, and emphasizes the need for proactive, data-driven healthcare systems. The findings suggest that AI- based forecasting can significantly improve timely intervention, reduce medical emergencies, and enable personalized patient care.

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