Abstract
Depression is a disease that affects 7.5 % percent of global disability. Depression is now-days a common disorder that affects the state of mind and produces sleep disorders. Around 50% of depressive patients suffer from sleep disturbances. In this work, a data mining process to classified depressive and not depressive episodes during nighttime is carried out based on a formal method of data mining called Knowledge Discovery in Databases (KDD). This process guides the process of data mining with stages well established: Pre-KDD, Selection, Pre-processing, Transformation, Data Mining, Evaluation, and Post-KDD. The dataset used for this paper is DEPRESJON dataset which contains the motor activity of 23 unipolar and bipolar depressed patients and 32 healthy controls. The classification of depressive and not depressive episodes is deployed with the Random Forest method and a model constructed of 8 features. Results on specificity are equal to 0.9927 and sensitivity equal to 0.9991.
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