sindhri / indians_diabetes_prediction

Use 9 demographic/medical measurements to predict diabetes via deep learning MLP with cross-validation and grid search.

Indians Diabetes Prediction

Use nine demographic/medical measurements to predict diabetes through a simple MLP with cross-validation and grid search.
Credit: Deep Learning with Python by Jason Brownlee
Data source UCL Machine Learning Repository http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data

The dataset has 768 rows and 10 columns, column 1 to 9 are parameters, column 10 is prediction 0- no diabetes, 1-has diabetes Column 1 - 9

1. Number of times pregnant.
2. Plasma glucose concentration a 2 hours in an oral glucose tolerance test.
3. Diastolic blood pressure (mm Hg).
4. Triceps skin fold thickness (mm).
5. 2-Hour serum insulin (mu U/ml).
6. Body mass index.
7. Diabetes pedigree function.
8. Age (years).
9. Class, onset of diabetes within five years.

A simple 3-layer MLP was built with dense layers, loss function is binary cross entropy. The following parameters were searched for optimization:
optimizers = ['rmsprop', 'adam']
inits = ['glorot_uniform', 'normal', 'uniform']
epochs = [50, 100, 150]
batches = [5, 10, 20]

After the grid search, which took ~20 minutes to run on the local CPU, the following configuration generates the best result:
Best: 0.748698 using {'batch_size': 5, 'epochs': 150, 'init': 'normal', 'optimizer': 'adam'}

So the final accuracy is 0.75

checkpoints_plot

Use checkpoints to save the best performance Plot history: loss function and metrics for each epoch throughout the training

checkpoints_load

Load the weights from the saved checkpoints for the model, then evaluate the model and calculate the accuracy But the final accuracy was different from when the checkpoints were made. Is it due to stochastic?