The chronic kidney disease dataset from Kaggle is utilized to test the model. Standard metrics including accuracy, precision, recall, F 1 -score, and area under receiver operating characteristic curve (AUC-ROC) are considered for proving the capability of the proposed work. In comparison to the ...
The data employed for fine-tuning was sourced from a preprocessed medical dataset, where each entry comprises a patient’s symptom description paired with the corresponding disease label. The selection of an appropriate loss function was deemed critical for successful training. Consequently, cross-...
To help doctors or medical experts in prediction of CKD among patients easily, this paper has developed an expert system named Chronic Kidney Disease Prediction System (CKDPS) that can predict CKD among patients. The dataset used to develop CKDPS is taken from the Kaggle machine learning data...
Each experiment used a different dataset taken from Kaggle (CKD dataset) and Alliance website (kidney disease genes dataset) for building binary and multi classification kidney disease models. SVC and KNN achieved 99.00%, 99.21% accuracy and recall, respectively for first experiment. While KNN and...
Explore and run machine learning code with Kaggle Notebooks | Using data from 🩺 Chronic Kidney Disease Dataset 🩺