Detection of Chronic Kidney (CK) disease is one of complex technique even in this modern technological era. Precise identification of CK disease is essential to provide treatment for patients with care. Several techniques were recently developed for precisely diagnosing CK diseases. However, certain forms of disadvantages still appear, including an incorrect selection of features, the need for large storage space, a requirement for an effective learning model, less accuracy, and high complexity related to time and cost. Few limitations and drawbacks are occurred and it provide less performance to detect CK disease. Therefore, the Neural Network Graph-based Deep Q-Learning (GNN-DQL) technique is proposed to classify five different states such as end, severe, moderate, mild and normal. First, real-time data will be gathered using and Bio-Medical sensors (BMs) and Internet of Medical Things (IoMT). Then, pre-process the data to improve the quality using missing value treatment, categorical data coding, transformation, and outlier detection to eradicate unwanted biases. The age and Serum Creatinine (SC) level of patient will be diagnosed using Glomerular Filtration Rate (GFR). Finally, classify the CK disease based on classes using GNN- DQL technique which provide better accuracy. The Adaptive Mayfly Optimization (AMO) algorithm is used to optimize the parameters. The simulation tools analyze the classification performance based on accuracy, precision, recall, specificity, F1- score and so on. The obtained accuracy of proposed model is 99.93% to detect the CK disease based on the five different classes from the real-time data.

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