ALS Disease detection

(Please download the '.html' file to visualize the graphs and other interesting outputs.)

Using the ALS dataset to study a rare but devastating progressive neurodegenerative disease, amyotrophic lateral sclerosis (ALS). Major clinically relevant questions include:

What patient phenotypes can be automatically and reliably identified and used to predict the change of the ALSFRS slope over time ?

Steps Implemented :

1. Load and prepare the data Report (short!) data summaries and show some preliminary visualizations.
2. Train a k-Means model on the data, select k.
3. Evaluate the model performance using bar and silhouette plots and summarize the results.
4. Tune and plot parameters with k-means++.
5. Rerun the model with the optimal parameters and interpret the clustering results.
6. Apply Hierarchical Clustering on three different linkages and compare the corresponding silhouette plots.
7. Fit a Gaussian mixture model, select the optimal model, report BIC, and display density and classification plots.
8. Compare the result of the above methods