Health Analysis with LatticaAI Demo Tutorial

Overview of the Model

Our Health Analysis model is trained on the Disease Prediction Kaggle datasetarrow-up-right. This dataset is designed to facilitate the application of machine learning to the medical field, aiding physicians by automating disease diagnosis based on symptoms.

The dataset consists of 131 binary columns representing different symptoms that a person may experience, and maps symptoms to 41 different diseases, allowing classification based on input symptoms.

We trained multi-class logistic regression model, implemented the equivalent fully homomorphic model and deployed it to our cloud service for secure inference.

  • Input Format: binary vector of length 131.

  • Output: Probability vector of length 41 that represents the possible diseases.

The equivalent pytorch code for the inference is:

file-download
3KB
List of 131 possible symptoms
file-download
685B
List of 41 disease
file-download
42KB
file-download
456B

Achieving Full Privacy with LatticaAI

circle-info

First install our client package

circle-info

See our step-by-step guide for a detailed explanation of each step in this flow. To use the image sharpening model use the healthPrediction model ID

Last updated