A Deep Neural Network for Early Detection and Prediction of Chronic Kidney Disease.
Vijendra SinghVijayan K AsariRajkumar RajasekaranPublished in: Diagnostics (Basel, Switzerland) (2022)
Diabetes and high blood pressure are the primary causes of Chronic Kidney Disease (CKD). Glomerular Filtration Rate (GFR) and kidney damage markers are used by researchers around the world to identify CKD as a condition that leads to reduced renal function over time. A person with CKD has a higher chance of dying young. Doctors face a difficult task in diagnosing the different diseases linked to CKD at an early stage in order to prevent the disease. This research presents a novel deep learning model for the early detection and prediction of CKD. This research objectives to create a deep neural network and compare its performance to that of other contemporary machine learning techniques. In tests, the average of the associated features was used to replace all missing values in the database. After that, the neural network's optimum parameters were fixed by establishing the parameters and running multiple trials. The foremost important features were selected by Recursive Feature Elimination (RFE). Hemoglobin, Specific Gravity, Serum Creatinine, Red Blood Cell Count, Albumin, Packed Cell Volume, and Hypertension were found as key features in the RFE. Selected features were passed to machine learning models for classification purposes. The proposed Deep neural model outperformed the other four classifiers (Support Vector Machine (SVM), K -Nearest Neighbor ( K NN), Logistic regression, Random Forest, and Naive Bayes classifier) by achieving 100% accuracy. The proposed approach could be a useful tool for nephrologists in detecting CKD.
Keyphrases
- neural network
- chronic kidney disease
- machine learning
- end stage renal disease
- deep learning
- blood pressure
- red blood cell
- early stage
- artificial intelligence
- type diabetes
- big data
- cardiovascular disease
- climate change
- hiv infected
- heart rate
- single cell
- high intensity
- adipose tissue
- mesenchymal stem cells
- bone marrow
- skeletal muscle
- cell therapy
- stem cells
- peripheral blood
- sentinel lymph node
- middle aged
- lymph node
- arterial hypertension