Examining the Role of Mood Patterns in Predicting Self-Reported Depressive symptoms
Table 6. Prediction result of depressive symptom (d=14, s=3
['Features', 'P', 'R', 'F1']
A baseline model is formulated using a stratified dummy classifier that generates predictions according to the training set’s class distribution. Out of several candidate algorithms, logistic regression demonstrated best performance. Hundreds of classification models were trained and evaluated for this task. The models with different representations from the mood profile can be evaluated by precision and recall. We grid searched d and s that maximises the metrics. Models with configuration 4 (time window 30 days and increment slide 3 days) yield the best scores. The model with mood, mood momentum and mood transition representations yields the highest scores, and the model with averaged mood over a time window gives second highest scores, 0.59 precision, 0.65 recall, and an F-score of 0.62.