Feature Selection in Bipolar Disorder Episode Classification Using Cost-Constrained Methods

An important step in the classification process of bipolar disorder episodes is feature selection process indicating the most relevant factors in patients’ behavior. The features in this task are associated with costs. Besides basic (low-cost) information about patients’ phone calls and text messages, we are studying the impact of acoustic features (high-cost) on classifying patients’ states. Unlike in previous papers, now we take the costs into account and thus we apply cost-constrained methods. The purpose of this paper is to examine whether the cost-constrained feature selection procedure is capable of improving the performance of the classification model while reducing the cost of making predictions. Moreover, we are trying to determine whether the reduced number of expensive features maintains a relatively high performance. We use a filter feature selection method that applies information theory. In the cost-constrained modification, we add a cost factor parameter that controls the trade-off between feature importance and its cost. The experiments were performed on a large medical database collected from patients with bipolar disorder during their daily mobile calls. The results indicate that the cost-constrained method allows to achieve better results than traditional feature selection when the budget is limited.