A Comprehensive Analysis of Chronic Kidney Disease Prediction through Machine Learning: Insights from Decision Tree Modeling

This study uses a multidimensional dataset to evaluate the possibility of Decision Tree modeling for predicting chronic kidney disease (CKD). The procedure entailed methodically assembling a dataset of patient demographics, clinical characteristics, and test results. This data was subsequently used to train the Decision Tree model, which identified important predictors of CKD. The results show that the model is effective in CKD prediction, with an accuracy of 0.85. Furthermore, Decision Trees gave a clear and understandable structure for comprehending the model's decision-making process. Serum creatinine levels, age, and blood pressure are all important predictors of CKD. These findings show that the model could be useful in clinical settings to help patients make decisions about their care. However, limits exist due to the dataset's size. To solve this and increase the model's generalizability, future research should focus on collecting larger datasets, studying more features, and experimenting with alternative AI techniques to improve prediction accuracy. Overall, this study advances CKD prediction by proving the potential of Decision Trees and underlining the need for interpretable models in healthcare applications. It lays the path for future research targeted at finetuning the model for real-world therapeutic applications.