Amyotrophic Lateral Sclerosis (ALS) Case-Study
Use the ALS dataset. This case-study examines the patterns, symmetries, associations and causality in 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?
- Load and prepare the data
- report data summary and show some preliminary visualizations
- Train a k-Means model on the data, select \(k\) according to the guide in Chapter 12 notes
- Evaluate the model performance using bar and silhouette plots and summarize the results
- Tune and plot parameters with k-means++
- Rerun the model with the optimal parameters and interpret the clustering results
- Apply Hierarchical Clustering on three different linkages and compare the corresponding silhouette plots
- Fit a Gaussian mixture model, select the optimal model, report BIC, and display density and classification plots
- Compare the result of the above methods
You can also try this protocol on some additional datasets from the list of our Case-Studies.
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