Earlier Forecasting of Diseases and Assessment of Risk Using a Novel Deep-Learning Approach
Keywords:
Chronic kidney disease (CKD), patient outcomes, risks, early prediction, randomized Gaussian-search Aquila optimization with Deep Neural Network (RGAO-DNN)Abstract
Globally, chronic kidney disease (CKD) is a problem with mortality rate and high morbidity. Rapid responses and better patient outcomes depend on early detection and precise risk assessment. To enable earlier CKD forecasting and risk assessment, this research suggests an innovative strategy incorporating randomized Gaussian-search Aquila optimization with Deep Neural Network (RGAO-DNN) network. The model intends to boost the network's efficiency and increase its capacity to capture complicated temporal connections in CKD data by integrating RGAO. An extensive dataset containing measures from CKD patients is used to assess the suggested approach. To deal with errors, normalize the data, and tackle group disparity problems, the min-max normalization methodology is used. The suggested technique is trained on the cleaned information, allowing it to recognize significant risk variables for the development of CKD and learn from temporal patterns. The effectiveness of the strategy is assessed using several measures. The experimental findings show that the suggested technique works better than other approaches regarding CKD risk evaluation and early prediction.
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