Abstract :

AI-driven Predictive Analytics For Disease Outbreaks Represents A Transformative Approach To Modern Public Health Surveillance And Response. Traditional Outbreak Detection Systems Often Rely On Historical Data Analysis And Manual Reporting, Which Can Result In Delayed Identification And Limited Preparedness. In Contrast, This Study Explores The Integration Of Artificial Intelligence (AI) And Machine Learning (ML) Techniques To Predict And Monitor Disease Outbreaks In Real Time With Higher Accuracy And Efficiency. The Proposed System Utilizes Large-scale Heterogeneous Data Sources Such As Electronic Health Records, Environmental Data, Social Media Trends, Mobility Patterns, And Demographic Information. Advanced Algorithms, Including Supervised Learning Models, Time-series Forecasting, And Deep Learning Architectures, Are Employed To Identify Patterns, Anomalies, And Early Warning Signals Of Potential Outbreaks. Natural Language Processing (NLP) Techniques Are Also Incorporated To Analyze Unstructured Data From Online Platforms And News Sources, Enabling Early Detection Of Emerging Health Threats. Furthermore, The System Incorporates Data Preprocessing, Feature Selection, And Model Optimization Techniques To Enhance Prediction Accuracy And Reduce False Positives. Visualization Dashboards And Geospatial Mapping Tools Are Used To Present Outbreak Predictions, Aiding Health Authorities In Timely Decision-making And Resource Allocation. The Integration Of Cloud Computing Ensures Scalability And Real-time Data Processing Capabilities.

Published:

08-5-2026

Issue:

Vol. 26 No. 5 (2026)

Page Nos:

490-498

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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