Chronic-Kidney-Disease

Prediction on Chronic Kidney Disease using Sklearn and Custom ML.

Instructions to run

(requires python 3+)

1. Install all libraries using(requirements file)

$ pip3 install -r requirements.txt

2. Run the ui.py file

$ python3 ui.py

Screen

Few Notes

The UCI Machine Learning Repository data set includes: Link :: https://archive.ics.uci.edu/ml/datasets/Chronic_Kidney_Disease We use 24 + class = 25 ( 11 numeric ,14 nominal)

Training : 127 records Testing : 33 records

• ge - age
• bp - blood pressure
• sg - specific gravity
• al - albumin
• su - sugar
• rbc - red blood cells
• pc - pus cell
• pcc - pus cell clumps
• ba - bacteria
• bgr - blood glucose random
• bu - blood urea
• sc - serum creatinine
• sod - sodium
• pot - potassium
• hemo - hemoglobin
• pcv - packed cell volume
• wc - white blood cell count
• rc - red blood cell count
• htn - hypertension
• dm - diabetes mellitus
• cad - coronary artery disease
• appet - appetite
• pe - pedal edema
• ane - anemia
• class - class

Classification Algo

Classes

1. Chronic (ckd)
2. Not Chronic (notckd)

• Logistic Regression
• Naive Bayes
• KNN

Accuracies

(These are the saved models accuracies)

1. KNN accuracy:

   • Custom : 90.62
   • SKLearn : 90.62

2. NB accuracy:

   • Custom : 100
   • SKLearn : 100

3. LR accuracy:

   • Custom : 68.75
   • SKLearn : 100

Directory details

1. dataset : processed csv file
2. charts : plots to visualize data
3. lib : custom implementations of all the algos
4. model : saved pre-trained model (both custom and inbuilt)
5. custom/ inbuilt : runner files to to prediction (training also)