Developing heart risk calculator using AI Builder

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The idea of this article is to experiment with AI Builder to develop an app for self-assessment of heart-related diseases. An AI-powered based app could tap into historical data and identify patterns, and then use them to assess the user’s entered data to predict if there is a risk of heart diseases or not.

The app is not to be used for medical advice or prescription, rather this is an experiment to use Prediction model of AI Builder with a #nocode approach.

The first step to accomplish this was to find data on heart-related risks. Luckily I got it, thanks to Kaggle. You can go here (it is one of the many publicly available datasets) and download the data.

  1. Next, goto Power Apps maker portal, create a solution and create an entity for storing heart diseases statistics. Create a model-driven app and add the entity into the app.
  1. Import data into the entity
  1. With data imported into the system, click on AI Builder on the left. Click on Prediction
  1. A new window will open, give a name to your model
  1. Select entity and the field which holds prediction outcome
  1. Select fields that contribute to the outcome (for example, de-select created on, modified on etc. if they are selected)
  1. Next, you can filter the data to be analyzed. In this case, we just selected ‘Skip this step’ and clicked ‘Next’
  1. View the summary and click on Train
  1. This data will be then fed into training the model
  1. Once training completes, publish the model. Accuracy is pretty impressive.
  1. Once published, details screen will show (a) option to run model by clicking on ‘Use Model’, (b) percentage distribution of individual field’s contribution to the outcome, and (c) how model is being used (currently it automatically syncs the outcome with the database daily).

It is important to note here that unlike other AI models, Prediction models run daily. If you want to run it on demand, click on Use Model and then click on Run Now.

  1. Now lets’s go back to the app and create a new user profile.
  1. Now either wait for Prediction model to run or force it to run now (step 9 above).
  2. Once the model has run, go back to your record. Here is a snapshot of my data:

The model has predicted user does not have the risk of heart diseases, viola!

Now that I have seen this running, I can already look back and think of past implementations where this model through AI Builder can add a lot of value. What do you think? Please feel free to share your feedback on Twitter.

Happy AI Building!

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