Early and precise prediction becomes necessary for successful disease management. This research demonstrates a new method involving Deep Separable Convolutional Neural Networks (DS-CNNs) in improving CKD prediction. Based on the Chronic Kidney Disease Dataset available at Kaggle, the ...
1. Dataset:- Kidney_data.csv This is open source data set taken from kaggle. Attributes in given data set:- age - age bp - blood pressure sg - specific gravity al - albumin su - sugar rbc - red blood cells pc - pus cell pcc - pus cell clumps ba - bacteria bgr - blood glucose...
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-...
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 ...
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...
2.1. Dataset Testing Machine Learning Algorithms to Predict Stroke Risk The open dataset [18] is used for the testing of the ML algorithms, focusing attention on stroke prediction. The dataset is available in theKaggledataset repository [18] as a .csv file containing 5110 observations with the ...
“legs–no legs” was solved based on Resnet50 with accuracy of 0.998. The application of this filter made it possible to collect a dataset of 11,118 good-quality leg images with various stages of CVD. For classification of various stages of CVD according to the CEAP classification, the ...