Performance Analysis of Chronic Kidney Disease through Machine Learning Approaches

Data mining and machine learning play a vital role in health care and also medical information and detection, Now a day machine learning techniques use awareness of some major health risks such as diabetic prediction, brain tumor detection, covid 19 detections, and many more. The kidney is the most important organ of our body and if it has any problem then the impact is more dangerous to our body. Chronic kidney disease (CKD), otherwise referred to as renal disease. CKD requires disorders that damage and reduce the capacity of our kidneys to keep us healthy. So, it is required to be concerned about kidney disease to our very primary stage. We take a few attributes to measure our analysis about chronic kidney disease and this attribute is one of the major occurrences of chronic kidney disease. Therefore 8 machine learning classifier are used to measure analysis using weka tools namely: Logistic Regression(LG), Naive Bayes(NB), Multilayer Perceptron(MLP), Stochastic Gradient Descent(SGD), Adaptive Boosting(Adaboost), Bagging, Decision Tree(DT), Random Forest(RF) classifier are used. We feature extraction of all attributes using principal component analysis(PCA). We gain the highest accuracy from the Random Forest(RF) and it is 99% and ROC(receiver operating characteristic) curve value is also highest from other algorithms.

Automated summary

Data mining and machine learning are crucial in healthcare, particularly in analyzing and monitoring major health risks such as chronic kidney disease. Machine learning techniques can enhance the accuracy of diagnosing kidney diseases, with the Random Forest model achieving the highest accuracy of 99%.