An optimized Framework for kidney disease Detection and Prediction Using DL Techniques - El-Kenawy, El-Sayed M.; Eid, Marwa M.; Elshewey, Ahmed M.; Osman, Ahmed M. (Mesopotamian Journal of Artificial Intelligence in Healthcare, 2024)
In recent years, chronic kidney disease (CKD) has become a crucial public health concern and issue, resulting in serious mortality rates. Therefore, we introduce a powerful system for kidney disease diagnosis that develops up-to-date approaches and applications. Our powerful system is meant for early detection, individual patient monitoring, and optimal CKD patient outcomes. The study introduces a CKD detection ecosystem primarily intended for optimized early detection techniques to be able to manage significant weaknesses in current systems and also for proactive treatment. This ecosystem also provides medical practitioners with up-to-date diagnostic technology, incorporating advanced Convolutional Neural Networks (CNN) that guarantee accuracy and efficiency of the diagnosis phase. Furthermore, it facilitates patient empowerment via an individualized mobile application meant for passive surveillance and active engagement in the treatment cycle. Our study introduces this ecosystem with two components, including (1) a deep sequential model for CKD diagnosis and (2) a CNN-based diagnosis for kidney conditions. We have developed the system in order to manage the shortcomings of CKD treatment, deal with the challenges faced by CKD patients by empowering them, and support the global attempts intended to reduce the CKD mortality rates. In addition, we anticipate it to significantly transform the kidney disease treatment field all over the world, demonstrating an effective performance with an accuracy of 98.75%.