This webapp was developed using Flask Web Framework. The models used to predict the diseases were trained on large Datasets. All the links for datasets and the python notebooks used for model creation are mentioned below in this readme. The webapp can predict following Disease. Our kidneys perform an important function to help filter blood and pass waste as urine. Chronic kidney disease, also called chronic kidney failure, describes the gradual loss of this function. At advanced stages, dangerous levels of fluid, electrolytes and wastes can build up in the body. Once this happens, patients must go through dialysis or consider a transplant. Our goal in this project is to see if we can predict if a patient will have chronic kidney disease or not using 24 predictors. If we are able to find variables with a strong influence on kidney failure, we may be able to detect and help patients at risk to prevent it.
- *Random Forest Classifier* is used for development of model.
- Only three algorithms are used to predict the output. They are *Logistic Regression*, *XGBoost* and *Random Forest*.\
1. Accuracy of the model using Logistic Regression is 95%.
2. Accuracy of the model using Random Forest Classifier is 99%.
3. Accuracy of the model using XGBoost Classifier is 99%.
- Machine Learning
Installation Step : -
- Python 3.7.0
- command 1 - python -m pip install –-user -r requirements.txt
- command 2 - python app.py