A comparative assessment of artificial intelligence models used for early prediction and evaluation of chronic kidney disease

Chronic Kidney Disease (CKD) is one of the most prevalent and fatal diseases influencing people on a larger that remains dormant until irreversible damage has been done to an individual's kidney. Progression of CKD is related to a variety of great complications, including increased incidence of various disorders, anemia, hyperlipidemia, nerve damage, pregnancy complication, and even complete kidney failure. Millions of people die from this disease every year. Diagnosing CKD is a cumbersome task as no major symptoms can be used as a benchmark to detect the disease. In cases when diagnosis persists, some results may be interpreted incorrectly. This study proposes using a deep neural network-based Multi-Layer Perceptron Classifier to diagnose CKD in patients. The model was trained using data from 400 people and considered various symptoms and signs, including age, blood sugar, red blood cell count, etc. Experiments reveal that the proposed model achieves perfect testing accuracy in classification tasks. Our goal is to facilitate introducing Deep Learning approaches to learning from the dataset attribute reports and accurately detecting CKD. The paper's primary contribution is a Deep Neural Network model for chronic kidney disease diagnosis that achieves 100% accuracy, outperforming standard machine learning models such as support vector machines and naive Bayes classifiers. This paper provides a detailed explanation of the multi-layer perceptron classifier, which uses the deep neural network provided by the PyTorch library as its basis. Neural models can be a better alternative for adaption techniques for classifying chronic kidney disease Because they can handle non-linearity in the data, compute complex data heaps fetched from datasets, and adapt and learn on their own about the key information using the layers of neurons present in the structure.