Machine Learning Models for Chronic Renal Disease Prediction

Chronic renal disease (CRD) has grown into a prominent concern. A person suffering from chronic renal disease does not have enough kidney functionality, which might be fatal as a result of kidney malfunction. Machine learning techniques are effective in predicting CRD. The methodology presented in this study for predicting CRD status determined on clinical information involves data preparation, a missing value management strategy with the use of collaborative filtering, as well as attribute choice. Among the nine machine learning algorithms studied, random forest classifier and the decision tree classifier have been demonstrated to maximum degree of accuracy of 100% and least amount of bias toward the characteristics. The article also discusses difficulties with actual data collecting and underlines the need of leveraging specialized expertise when applying machine learning to forecast CRD status.