hearth-disease-prediction

Content

• Quickstart
• Datasets
• Models
  • Dense Features Model
    • Training
    • Evaluating
• Results

Quickstart

To start the docker container execute the following command

$ ./bin/start [-n <string>] [-t <tag-name>] [--sudo] [--build] [-d] [-c <command>]

Tags

• latest The latest release of TensorFlow CPU binary image. Default.
• nightly Nightly builds of the TensorFlow image. (unstable) version Specify the version of the TensorFlow binary image, for example: 2.1.0
• devel Nightly builds of a TensorFlow master development environment. Includes TensorFlow source code.

Variants

Each base tag has variants that add or change functionality:

• <tag>-gpu The specified tag release with GPU support. (See below)
• <tag>-py3 The specified tag release with Python 3 support.
• <tag>-jupyter The specified tag release with Jupyter (includes TensorFlow tutorial notebooks)

You can use multiple variants at once. For example, the following downloads TensorFlow release images to your machine. For example:

$ ./bin/start -n myContainer --build  # latest stable release
$ ./bin/start -n myContainer --build -t devel-gpu # nightly dev release w/ GPU support
$ ./bin/start -n myContainer --build -t latest-gpu-jupyter # latest release w/ GPU support and Jupyter

Once the docker container is running it will execute the contents of the /bin/run file.

You can execute

$ docker exec -it <container-id> /bin/sh -c "[ -e /bin/bash ] && /bin/bash || /bin/sh"

to access the running container's shell.

Datasets

This data set dates from 1988 and consists of four databases: Cleveland, Hungary, Switzerland, and Long Beach V. It contains 76 attributes, including the predicted attribute, but all published experiments refer to using a subset of 14 of them. The target field refers to the presence of heart disease in the patient. It is integer valued 0 = no disease and 1 = disease.

Models

Training

Run the following command to run training on <config> with default parameters.

$ ./bin/run --model <model> --mode train --config <config>

<model> = df_v1 | df_v2 | tfjs <config> = default | heart

Evaluating

To run evaluation on a specific dataset

$ ./bin/run --model <model> --mode eval --config <config>

<model> = df_v1 | df_v2 | tfjs <config> = default | heart

Results

In the /results directory you can find the results of a training processes using a <model> on a specific <dataset>:

.
├─ . . .
├─ results
│  ├─ <dataset>                            # results for an specific dataset.
│  │  ├─ <model>                           # results training a <model> on a <dataset>.
│  │  │  ├─ models                         # ".h5" files for trained models.
│  │  │  ├─ results                        # ".csv" files with the different metrics for each training period.
│  │  │  ├─ summaries                      # tensorboard summaries.
│  │  │  ├─ config                         # optional configuration files.
│  │  └─ └─ <dataset>_<model>_results.csv  # ".csv" file in which the relationships between configurations, models, results and
summaries are listed by date.
│  └─ summary.csv                          # contains the summary of all the training
└─ . . .

where

<dataset> = heart | ?
<model> = <model> = df_v1 | df_v2 | tfjs

To run TensorBoard, use the following command:

$ tensorboard --logdir=./results/<dataset>/<model>/summaries