2 32 CHAPTER 2 As the outcome measure, we calculated the average number of steps for all workdays over all weeks. That is, for each individual, we calculated one average for all workdays. We considered the number of steps between 7:00 AM and 6:00 PM. Note that this outcome measure is not used as input in the training process. We constructed a binary outcome variable represented by the indicator variable = ( ≥ ), in which in which refers to the number of steps on a workday for individual , and refers to the specific step goal for that j. The indicator function returns one (the `true’ label) when the inside condition holds, and zero (the ‘false’ label) otherwise. Three days of repeated measures are necessary to represent adults’ usual activity levels with an 80% confidence [6]. Forty-four participants met the criteria. The processing and transformation for these forty-four participants resulted in a total of 120,480 data blocks (for the number of steps, mean = 9,031, median = 8,543, range = 0- 47,121). The total number of positives when the threshold is met at 6:00 PM, is 1528. The total number of negatives when the threshold is not met at 6:00 PM, is 1,879. Note that we did not include any of the group level/baseline variables like age or gender, as we only considered personalized models. Although these variables might affect the outcome, they do not vary within the individual and as such do not add information. 3.4. Evaluation of the Performance of Algorithms and Models We trained eight different machine learning algorithms. To compare their performance, we used a method known as `confusion matrices’. The confusion matrices give an overview of the true positives (TP; the model predicted a `true’ label and the actual data contained a `true’ label), true negatives (TN; the model predicted a `false` label and the actual data turned out to have a `false’ label), false positives (FP; the model predicted a `true’ label, but the actual data contained a `false’ label), and false negatives (FN; the model predicted a `false’ label, but in fact the data contained a `true’ label) of a model. An example of a confusion matrix is provided in Table 1. These confusion matrices served as a basis for the calculation of two other performance measures: The accuracy and the F1-score [15]. Table 1. Confusion matrix. True class Yes No Predicted class Yes True Positives (TP) False Negatives (FN) No False Positives (FP) True Negatives (TN) True Positive: the threshold of daily steps was met and predicted; True Negative: the threshold of daily steps was not met and predicted; False Negative: the threshold of daily steps was met and not predicted; False Positive: the threshold of daily steps was not met and not predicted.
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