Comparison of Machine Learning Models for Identification of Depressive Patients through Motor Activity
DOI:
https://doi.org/10.17488/RMIB.46.1.1487Keywords:
data mining, data analysis, depression, machine learning and motor activityAbstract
The present study aims to evaluate various classification algorithms for data pertaining to subjects diagnosed with depression and non-depressive subjects. To this end, the data obtained from the "depresjon" dataset proposed by Garcia-Ceja, E., et al were analyzed. This dataset comprises motor activity recorded by the Actiwatch device (Cambridge Neurotechnology Ltd, England, model AW4). Predictions were made using various machine learning models, including synthetic data. Subsequently, metrics such as specificity, sensitivity, and precision were compared. The results highlight the best features of the data and the best machine learning model (using an ensemble model) for classifying potential depressive episodes in activity during the afternoon and night, with a precision of 96.6 %, sensitivity of 100 %, and specificity of 93.33 %.
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