Abstract: Chronic Kidney Disease (CKD) prediction system is a machine learning-based system that uses Convolutional Neural Networks (CNN) and Principal Component Analysis (PCA) to predict the likelihood of CKD in patients and classify their stage of disease. The system takes patient data, such as age, gender, creatinine level, blood urea nitrogen level, and glucose level, as input, and preprocesses it using PCA to reduce dimensionality and improve model performance. The preprocessed data is then fed into a CNN model for prediction and stage classification. The system was evaluated on a dataset of patients with varying stages of CKD and achieved high accuracy and stage classification performance, demonstrating its potential as an early detection and treatment aid for CKD. The CKD prediction system has the potential to improve patient outcomes and reduce healthcare costs associated with CKD treatment, making it a valuable tool for clinical practice.

Keywords: CKD stage identification; chronic kidney infection; machine learning, and CNN.

PDF | DOI: 10.17148/IARJSET.2023.10554

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