Data-Driven Models for Personalized Ophthalmic Care in ICT-Enabled Smart Health Systems
DOI:
https://doi.org/10.69996/fmep.2025032Keywords:
Personalized Ophthalmic Care, Data-Driven Models, ICT-Enabled Health Systems, Retinal Imaging, Machine Learning, Predictive AnalyticsAbstract
This study explores the application of data-driven models in delivering personalized ophthalmic care within ICT-enabled smart health systems. Leveraging diverse data sources, including electronic health records, retinal imaging, optical coherence tomography (OCT), and real-time patient monitoring devices, the research emphasizes the capacity of integrated ICT frameworks to facilitate precision diagnostics and individualized treatment strategies. Advanced machine learning techniques such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Gradient Boosting, and Ensemble Learning are evaluated for their effectiveness in detecting ophthalmic anomalies, predicting disease progression, and optimizing clinical decision-making. CNNs achieved a remarkable 94.1% accuracy in retinal lesion classification, while RNN-based models demonstrated superior predictive performance in longitudinal vision assessment with an F1-score of 92.7%. Ensemble Learning further enhanced robustness by combining multi-modal data inputs, achieving over 93% overall accuracy. The study highlights the transformative potential of ICT-enabled data ecosystems in ophthalmology, enabling continuous, patient-centric monitoring, early intervention, and adaptive treatment planning.
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