Current Volume 8
A Data-Driven Framework for Early Detection and Prevention of Non-Communicable Diseases in Healthcare Systems
Abstract
Non-Communicable Diseases (NCDs), such as cardiovascular diseases, diabetes, chronic respiratory diseases, and cancer, pose a significant global health challenge, accounting for the majority of deaths worldwide. Early detection and prevention are critical to reducing the burden of NCDs, improving patient outcomes, and optimizing healthcare resources. A data-driven framework leveraging advanced technologies, including Electronic Health Records (EHRs), wearable sensors, genomic data, and environmental factors, can enhance predictive capabilities for early intervention. Machine learning (ML) and artificial intelligence (AI) play a pivotal role in analyzing large-scale, multi-source health data to identify patterns, assess risk factors, and develop personalized prevention strategies. Supervised learning models, deep learning techniques, and federated learning approaches enable robust prediction and decision-making while ensuring data privacy and security. Integration with telemedicine and remote monitoring further strengthens continuous patient tracking and proactive care. Despite the promise of AI-driven healthcare, several challenges remain, including data privacy and regulatory compliance (HIPAA, GDPR), standardization of health data, clinician trust in AI-based decisions, and computational scalability. Addressing these limitations through advancements in Explainable AI (XAI), blockchain for secure data exchange, and real-time analytics can improve AI adoption in clinical settings. This presents a comprehensive framework for implementing data-driven methodologies to enhance early detection and prevention of NCDs. By leveraging AI, multi-modal data fusion, and innovative healthcare technologies, this framework can support clinicians in making informed decisions, enable personalized preventive interventions, and reduce the global burden of NCDs. Future research should focus on improving AI transparency, ensuring ethical data usage, and developing policy frameworks for large-scale adoption of AI-driven NCD prevention strategies in healthcare systems.
Keywords
Data-driven, Early detection, Non-communicable diseases, Healthcare systems
Citations
IRE Journals:
Bamidele Samuel Adelusi , Damilola Osamika , MariaTheresa Chinyeaka Kelvin-Agwu , Ashiata Yetunde Mustapha , Nura Ikhalea
"A Data-Driven Framework for Early Detection and Prevention of Non-Communicable Diseases in Healthcare Systems" Iconic Research And Engineering Journals Volume 8 Issue 1 2024 Page 700-712
IEEE:
Bamidele Samuel Adelusi , Damilola Osamika , MariaTheresa Chinyeaka Kelvin-Agwu , Ashiata Yetunde Mustapha , Nura Ikhalea
"A Data-Driven Framework for Early Detection and Prevention of Non-Communicable Diseases in Healthcare Systems" Iconic Research And Engineering Journals, 8(1)
About IRE Journals
IRE Journals is an open access online journals established with an aim to publish high quality of research work in various diciplines. We have more than 6 years in publications. Over 2500+ papers already published.
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