Classifying kidney disease using a dense layers deep learning model

Abstract

Early diagnosis and thorough management techniques are crucial for people with chronic kidney disease (CKD), a crippling and potentially fatal condition. Research has focused a lot on machine learning and deep learning systems for the detection of kidney diseases. Deep learning platforms like hidden layers, activation functions, optimizers, and epochs are also necessary for the automatic detection of these diseases. The proposed model achieved 99 % accuracy, with a precision, recall, and F1 score of 0.99, indicating highly reliable performance. Additionally, the model demonstrated strong agreement and robustness, as reflected in metrics such as the ROC AUC score of 0.9821 and Matthews Correlation Coefficient of 0.9727. The experiment used a publicly accessible dataset with 24 independent fields and independent values as chronic or not-chronic classes, building dense-layered deep neural networks based on an optimized architecture. The outcomes demonstrated that, when compared to the other models, the proposed model was the most accurate.