Data-Driven Approaches for Early Detection and Prediction of Chronic Kidney Disease Using Machine Learning

In recent years, the application of machine learning (ML) techniques
for medical diagnostics has shown promising advancements. This
study introduces a distinctive method for predicting chronic kidney
disease (CKD) harnessing the prowess of ML. Our methodology
encompasses an innovative data preprocessing approach, intricate
feature engineering, and an amalgamation of ensemble techniques
for model training. By evaluating our model on a dataset sourced
from Kaggle, comprising 400 samples, we achieved an impressive
accuracy of 98%, outperforming traditional methods. The findings
underscore the potential of ML in revolutionizing CKD diagnostics,
laying a foundation for further exploration in this domain.