Diabetes prediction using neural network created using keras with tensorflow backend.
pip install h5py==2.8.0
pip install Keras==2.2.0
pip install Keras-Applications==1.0.2
pip install Keras-Preprocessing==1.0.1
pip install numpy==1.14.5
pip install PyYAML==3.12
pip install scikit-learn==0.19.1
pip install scipy==1.1.0
pip install six==1.11.0
pip install sklearn==0.0 `This work is used to predict the diabetes in a patient. The dataset used here is the Pima Indians diabetes database. The dataset consists of 768 entries having 9 features. The entires correspond to the test on each patient.
The 9 features are :
- Pregnancies - Number of times pregnant
- GlucosePlasma - glucose concentration a 2 hours in an oral glucose tolerance test
- BloodPressure - Diastolic blood pressure (mm Hg)
- SkinThickness - Triceps skin fold thickness (mm)
- Insulin - 2-Hour serum insulin (mu U/ml)
- BMI - Body mass index (weight in kg/(height in m)^2)
- DiabetesPedigreeFunction - Diabetes pedigree function
- Age - Age (years)
- Outcome - Class variable (0 or 1) 268 of 768 are 1, the others are 0
| Pregnancies | GlucosePlasma | BloodPressure | SkinThickness | Insulin | BMI | DiabetesPedigreeFunction | Age | Outcome |
|---|---|---|---|---|---|---|---|---|
| 6 | 148 | 72 | 35 | 0 | 33.6 | 0.627 | 50 | 1 |
| 1 | 85 | 66 | 29 | 0 | 26.6 | 0.351 | 31 | 0 |
There is a main training.py file which contains the ANN defined using the keras. The input Prima Indians diabetes csv file is splitted into train & test. The trained model is saved as model.h5. This saved model will be then used for the single as well as the bulk prediction programs.
