XGBoost Learning for Detection and Forecasting of Chronic Kidney Disease (CKD)
Keywords:
RCode, XGBoost technique, Classification, Accuracy, Chronic Kidney Disease (CKD)Abstract
It's astounding that 63,538 cases have been documented. based on data from India's chronic kidney disease (CKD). Nephropathy in humans usually appears between the ages of 48 and 70. Compared to women, men are more likely to develop CKD. Regretfully, India has slipped into the top 17 countries for chronic kidney disease (CKD) since 2015. CKD is characterized by a gradual deterioration in the function of the excretory organs. Effective treatment and early illness identification may help prevent this terrible condition. Among other practical applications, machine learning is being used in fraud detection and medical research findings analysis. Chronic illness forecasting is done using a variety of machine-learning techniques. With a focus on decision trees, Adaboost, XGboost, random forests, logistic regression, support vector machines, naïve Bayes, KNN, and artificial neural networks, our primary goal is to evaluate the accuracy of various machine learning techniques. Here XGBoost ML algorithm performs well for prediction of chronic kidney disease (CKD), it provide the 99% accuracy and which is almost greater than the other ML algorithms tested. RCode has received praise from the study's performance analysis. This project's primary goal is to develop an application for sickness prediction that uses an analysis of the chronic kidney disease dataset to detect cases of chronic kidney disease (CKD) and non-CKD.
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