Chronic kidney disease (CKD) is a global health problem with high morbidity and mortality rate, and it induces other diseases. Since there are no obvious values during the early stages of CKD, patients often fail to notice the disease. Early detection of CKD enables patients to receive timely treatment to ameliorate the progression of this disease. Machine learning models can effectively aid clinicians achieve this goal due to their fast and accurate recognition performance. In this study, we propose a machine learning methodology for diagnosing CKD. The CKD data set was obtained from the University of California Irvine (UCI) machine learning repository, which has a large number of missing values. Preprocessing was used to fill in the missing values, which selects several complete samples with the most similar measurements to process the missing data for each incomplete sample. Missing values are usually seen in real-life medical situations because patients may miss some measurements for various reasons. After effectively filling out the incomplete data set, machine learning algorithm ( , random ) were used to establish models. Among these machine learning models; random forest achieved the best performance with 99.75% diagnosis accuracy.
● Platform - Web Browser
● Backend - Flask, NodeJS, ExpressJs
● Frontend - ReactJs, Bootstrap
● Database -MongoDB
● Machine learning Model - Random Forest Regression, XGBoost
This Website uses many techniques and algorithms and all other various tools to build a system which predicts the disease of the patient using the values and by taking those values we are comparing with the system’s dataset that is previously available. By taking those datasets and comparing with the patient’s disease we will predict the accurate percentage disease of the patient. The dataset and values go to the prediction model of the system where the data is pre-processed for the future references and then the feature selection is done by the user where he will enter the various values. By taking those datasets and comparing with the patient’s disease we will predict the accurate percentage disease of the patient. The dataset and values go to the prediction model of the system where the data is pre-processed for the future references and then the feature selection is done by the user where he will enter the various values.
If you would like to experiment with the dataset and the code at Github.