KIDNEY-DISEASE

CHRONIC KIDNEY DISEASE PREDICTION USING CNN, LSTM ENSEMBLE MODEL
DEEP LEARNING PROJECT The field of CKD. The study shows a detailed explanation of the technical specifics of deep learning (DL) architectures along with a comprehensive performance assessment of the DL methods used to predict CKD. Five sets of feature selection/ranking tools have been utilized and compared to incentivize the application of DL methods. Further, the use of statistical analysis proved the outcome more reliable and effective. In addition, the study shed some insights into the application to the development of a health monitoring framework that can be used as an IoMT portal based on DL algorithms. In this project we are using various deep learning algorithms such as CNN, LSTM and Ensemble CNN with Random Forest to predict chronic kidney disease. To train all algorithms we have used Chronic Kidney Dataset from KAGGLE repository and below is the dataset URL THE dataset values and in last column we have class label called ‘CKD or NO_CKD’. So by using above dataset we will train all algorithms and then evaluate their performance in terms of accuracy, precision, recall, FCSORE and Confusion Matrix. In above dataset we can see dataset contains both non-numeric and numeric values but algorithms accept only numeric values so we need to convert all non-numeric values into numeric values by applying Label Encoder class