ML for Healthcare Project: Interpretable and Explainable Classification for Medical Data

Description

This project was created for the course Machine Learning for Health Care 2023. Through different ML (LASSO, tree, MLP, NAM) and DL (CNN) models we attempted to classify patients into different groups based on their medical data. The goal was to create models that are interpretable and/or explainable, enabling doctors to understand the model's decision making process and, thereby, creating trust in the model.

Subtasks

The project consisted of several subtasks, whereby the code for each subtask can be found in the corresponding folder:

1. Coronary Heart Disease

cd heart_failure

2. Pneumonia

cd pneumonia