AI-Driven Predictive Health Analytics for Community-Centric Disease Prevention

Persistent health inequities in underserved communities necessitate proactive healthcare solutions. AI and predictive analytics enabling us to detect disease at early stages, making and optimized resource allocation in healthcare, aligning with Sustainable Development Goal 3 for equitable health access. This study aimed to develop an AI-driven predictive analytics platform to forecast disease outbreaks, assess individual health risks, and optimize resource allocation through geospatial analytics. A mixed-methods approach integrated real-time data from health records, wearables, and sensors. Machine learning models predicted outbreaks and assessed risks, while geospatial tools mapped hotspots. A mobile app enabled symptom reporting and telemedicine access, ensuring ethical compliance through data anonymization and bias mitigation. The study achieved 92% precision in forecasting dengue outbreaks using weather and mobility data, reducing hypertension-related hospitalizations by 22 %, and engaging 78 % of users with the mobile app.The AI system has a lot of promise for promoting SDG 3 by enhancing medical services in areas that lack proper care. To grow this initiative, it is necessary to tackle infrastructure problems and increase the use of genomic data.

• Publication year

  2025

• Venue

  2025 International Conference on Artificial intelligence and Emerging Technologies (ICAIET)

• Publication date

  2025-08-28

• Fields of study

  Not labeled

• Identifiers
  DOI 10.1109/ICAIET65052.2025.11211503
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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