Chronic Kidney Disease
Early detection of kidney illness can be achieved by training machine learning algorithms to discover patterns in patient data, such as imaging, test results, and medical history. This will enable rapid diagnosis and start of treatment regimens, which can improve patient outcomes. With 98.97% accuracy in CKD detection, the suggested TrioNet with KNN imputer and SMOTE fared better than other models. This comprehensive research highlights the model's potential as a useful tool in the diagnosis of chronic kidney disease (CKD) and highlights its capabilities.
- Categories:
953 Views
This Data set was obtained from a Hospital in Karaikudi, Tamilnadu Iindia, and has 400 insstances with 25 attributes, intended for classification problems.
The Data Set has medical relevant variables that can be associated to the presence of CKD (Chronical Kidney Diasease). Some of the variables can be arguably more relevant for the model, and after analysis some of them can be correlated, so it's recommended to analyze the dataset and decide the best approach based on individual needs.
- Categories:
4298 Views