Optimizing healthcare data quality with optimal features driven mutual entropy gain.

In the dynamic domain of healthcare data management, safeguarding sensitive information while ensuring data efficiency is always of the highest priority. Healthcare data are frequently mishandled, posing significant risks. This research offers a new network that assesses the quality of visual data using robust features‐driven Mutual Entropy Gain (MEG). The proposed network addresses a critical gap in healthcare data management, significantly enhancing patient data security and operational efficiency in medical institutions. Our method begins with a thorough empirical investigation to find the optimal intermediate features for network input. We incorporate both distance entropy and probability entropy adopted and normalized in MEG, resulting in a comprehensive healthcare data quality evaluation. The results show that the network can distinguish between high‐quality and low‐quality data based on information content. Furthermore, our assessment reveals a large performance discrepancy between high and low‐quality data, even with variable datasets. Notably, using only half of the data achieves commendable accuracy when compared with using the complete dataset, demonstrating possible efficiency gains. This breakthrough has far‐reaching implications for healthcare providers, potentially reducing data storage costs, accelerating data processing times, and minimizing the risk of data breaches. In essence, our proposed network enhances efficiency and security in healthcare data and adapts to the evolving landscape of convergence ICT, paving the way for more robust, cost‐effective, and secure healthcare information systems that can significantly improve patient care and operational outcomes. [ABSTRACT FROM AUTHOR]