Prediction of Chronic Kidney Disease Using Various Machine Learning Algorithms

Chronic Renal Disease (CRD), often referred to as Chronic Kidney Disease (CKD), has become more prevalent over time. Only 18 days may be spent without a functioning kidney, which is why dialysis and kidney transplants are so common. Predicting CKD at an early stage is crucial. Machine learning is quite effective at forecasting CKD. The model proposes an approach to predict CKD status based on clinical data by combining preprocessing, a mechanism for handling missing values, and collaborative filtering. It assists i
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figuring out whether someone has CKD. This research utilizes several algorithms to determine which one was the most accurate for getting the result. The creation of a trustworthy model for anticipating chronic renal disease is the goal of this research. The model in most of the current systems employs various ML algorithms separately. If the method is not sufficiently regularized, the data is too noisy, or there are too many features in the data, overfitting may happen. However, several algorithms are employed in this model to build a more precise, reliable, and adaptable model.