In this project I have developed a web app for automated diagnosis of chronic kidney disease cases based on 11 input variables. I have used python and flask for making the web app and hosted it on heroku cloud. The web app is live at (https://kidney-diagnosis-app.herokuapp.com/)
The dataset used for this project is taken from UCI Mchine Learning Repository at chronic kidney disease.This dataset can be used to predict the chronic kidney disease and it is collected from the Apollo hospital at Tamil Nadu in nearly 2 months of period.
This dataset contains 24 attributes and 1 Target class variable
-- 1. Age(numerical)
-- 2. Blood Pressure(numerical) - bp in mm/Hg
-- 3. Specific Gravity(nominal) sg - (1.005,1.010,1.015,1.020,1.025)
-- 4. Albumin(nominal) al - (0,1,2,3,4,5)
-- 5. Sugar(nominal) su - (0,1,2,3,4,5)
-- 6. Red Blood Cells(nominal) rbc - (normal,abnormal)
-- 7. Pus Cell (nominal) pc - (normal,abnormal)
-- 8. Pus Cell clumps(nominal) pcc - (present,notpresent)
-- 9. Bacteria(nominal) ba - (present,notpresent)
-- 10. Blood Glucose Random(numerical) bgr in mgs/dl
-- 11. Blood Urea(numerical) bu in mgs/dl
-- 12. Serum Creatinine(numerical) sc in mgs/dl
-- 13. Sodium(numerical) sod in mEq/L
-- 14. Potassium(numerical) pot in mEq/L
-- 15. Hemoglobin(numerical) hemo in gms
-- 16. Packed Cell Volume(numerical)
-- 17. White Blood Cell Count(numerical) wc in cells/cumm
-- 18. Red Blood Cell Count(numerical) rc in millions/cmm
-- 19. Hypertension(nominal) htn - (yes,no)
-- 20. Diabetes Mellitus(nominal) dm - (yes,no)
-- 21. Coronary Artery Disease(nominal) cad - (yes,no)
-- 22. Appetite(nominal) appet - (good,poor)
-- 23. Pedal Edema(nominal) pe - (yes,no)
-- 24. Anemia(nominal) ane - (yes,no)
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Numerical: 1,2,10,11,12,13,14,15,16,17,18
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Nominal: 3,4,5,6,7,8,9,19,20,21,23,24
Class - nominal - (ckd, notckd)
400 observations
This project requires Python 3.x and the following Python libraries installed:
Use pip to install any missing dependencies. I also reccommend to install Anaconda, a pre-packaged Python distribution that contains all of the necessary libraries and software for this project which also include jupyter notebook to run and execute IPython Notebook.
In a terminal or command window, navigate to the top-level project repo kidney-diagnosis-app/(that contains this README) and run one of the following commands:
ipython notebook Chronic_kidney_Classification.ipynb
or
jupyter notebook Chronic_kidney_Classification.ipynb
This will open the iPython Notebook software and project file in your browser.
I have done model evaluation based on following sklearn metric.
- Confusion Matrix
- Sklearn classification report
- Mcnemar Test