HIGHLIGHTS
SUMMARY
Principal Component Analysis (PCA) was executed to examine the correlation of physical and psychological longitudinal data and confirm that psychological or a combination of psychological and physical longitudinal data can be employed for unsupervised clustering. Application of different machine_learning algorithms together with over-sampling and under-sampling for the prediction of the generated clusters followed. As predictors for the machine_learning models were demographic data and clinical injury-related data from the patients. Parallel with machine_learning part, qualitative evaluation of the generated clusters occurred based on descriptive statistics and medical expertise to assess how clinically . . .
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