Characterizing X-Linked Dystonia Parkinsonism Using Clustering Techniques in Data Science

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X-linked dystonia parkinsonism (XDP) is a neurodegenerative rapidly progressive movement disorder seen almost exclusively among male patients of Filipino descent with traces of ancestry to Panay Island in the Philippines. The prevalence of XDP is only 0.31/100,000 for the entire Philippines but it is 23.66/100,000 in Capiz Province in Panay. Using a retrospectively obtained data from a previous study collected from all the 16 towns and 1 city of Capiz province in the island of Panay in the Philippines, the XDP dataset showed superior clustering tendency. Two clustering approaches commonly used in data science namely Partitioning Around Medoids (PAM) and K-modes were applied to the XDP dataset. Both clustering approaches obtained good internal and external validation. Likewise, both clustering techniques generated good performance metrics. Overall, K-modes performed better than PAM with an 81% accuracy, 78% recall (sensitivity), 82% specificity, 68% precision, 72% F1-score and 0.58 Matthews Correlation Coefficient. Both PAM and K-modes were able to highlight the differences between the clusters. Thus, the resulting clusters can be useful as screening tools to differentiate those with and without XDP. Feature importance of the attributes was also performed with feet shuffling generating the highest discriminative power in distinguishing patients with and without XDP. The collaboration of data scientists with neurology experts in movement disorders is a step forward to the deployment and acceptability of machine learning tools in clinical practice in neurology.