Chronic kidney disease prediction machine learning web app

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.
Algorithm :
*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%.
Technology Used
Python
Machine Learning
Pandas
Numpy
Scikit-learn
Flask
HTML
CSS
Installation Step : -
Python 3.7.0
command 1 -
python -m pip install --user -r requirements.txt
command 2 -
python app.py

Detailed Project Description

Chronic kidney disease prediction machine learning web app applies machine learning to a real dataset: it prepares the data, trains and evaluates a model, and exposes the trained model through a simple interface so predictions can be made on new inputs.

How It Works
1. The dataset is collected/loaded, cleaned and explored (missing values, encoding, scaling).
2. Features are engineered and the data split into training and test sets.
3. One or more models are trained and compared using accuracy, precision/recall, F1 or RMSE.
4. The best model is saved and served through a web/GUI form that returns predictions.

Technology Roles
- BLE
- Python
- Flask
- Machine Learning
- Deep Learning
- HTML
- CSS

Implementation Steps
1. Collect the dataset (public repository or own data) and understand each attribute.
2. Set up the environment (Arduino IDE) and load the data into a notebook.
3. Clean the data: handle missing values, outliers, encoding and scaling; visualise distributions.
4. Split into train/validation/test sets and engineer relevant features.
5. Train several candidate models, tune hyper-parameters and compare metrics.
6. Save the best model and wrap it in a simple web/GUI form for live predictions.
7. Document results with plots, confusion matrix / error charts and conclusions.

Key Features
- Data pre-processing pipeline
- Model comparison
- Evaluation metrics and plots
- Prediction interface

Expected Output
On completion the student will have evaluation results (metrics, confusion matrix or error plots) and a working prediction form.

Deliverables
- Notebook / source code with comments
- Cleaned dataset and trained model file
- Evaluation report with metrics and plots
- Prediction interface