Psikoloji bilimi yaklaşımıyla öznel iyi hal durumunun makine öğrenmesiyle modellenmesi

Recent advances in pervasive computing enable the collection of personal health-related data using diverse sensors in the everyday-life environment. However, human behavior modeling and analysis, particularly the quantification of subjective well-being, is still challenging, as there are variations in its definition and measurement. The psychology literature defines different perspectives on subjective well-being, such as hedonic and eudaimonic. In this thesis, we propose a model for predicting an individual's subjective well-being from the psychological perspective using her/his daily activities collected via smart wristbands, social relationships monitored through smartphones, and personality traits data from surveys. The model is applied to the NetHealth study, a heterogeneous data set of 577 student participants from the University of Notre Dame. We developed a multi-class classifier based on commonly accepted machine learning algorithms. The results enable us to predict an individual's well-being with almost 80% accuracy. We show the feasibility of a pervasive application as a personalized well-being assistant.