Early prediction of CKD from time series data using adaptive PSO optimized echo state networks

Chronic Kidney Disease (CKD) is a significant problem in today's healthcare since it is challenging to detect until it has improved significantly, which increases medical expenses. If CKD was detected early, the patient might qualify for more effective treatment and prevent the disease from spreading further. Presently, existing methods that effectively detect CKD cannot detect symptoms early on. This problem motivates researchers to work on a predictive model that successfully detects disease symptoms in the early stages. This study introduces a novel Adaptive Particle Swarm Optimization (APSO)-optimized Echo State Network (ESN) model designed to overcome key limitations of existing methods. ESNs, while effective in processing temporal sequences, are highly sensitive to hyperparameter settings such as spectral radius, input scaling, and sparsity, which directly impact stability, memory retention, and predictive Classification Accuracy (CA). To address this, APSO optimizes these hyperparameters dynamically, ensuring a balanced trade-off between stability and computational efficiency. Moreover, Random Matrix Theory (RMT) is integrated into APSO to regulate the spectral radius, enhancing the ESN's capability to handle long-term dependencies while maintaining stability in training. This investigation exploited the Medical Information Mart for Intensive Care-III (MIMIC-III) dataset to train the model they developed. The proposed method employs this data collection to analyze the highly complex temporal sequences signifying CKD is present. The hyperparameters of the ESN, such as the range of the spectral region and the input data sizing, can be optimized in real-time with APSO by applying Random Matrix Theory (RMT). Compared with different recognized models, such as conventional ESN and standard M, the recommended APSO + ESN proved to have higher CA in medical investigations. The APSO + ESN improved the subsequent highest-performing model by 2% in recall and 3% in precision and attained a CA of 99.6%.