6533b872fe1ef96bd12d30b8
RESEARCH PRODUCT
A happiness degree predictor using the conceptual data structure for deep learning architectures
J. Alberto ConejeroJuan M. García-gómezPatricia Villacampa-fernándezPatricia Villacampa-fernándezEsperanza Navarro-pardoFrancisco Javier Pérez-benitosubject
MalePsychometricsmedia_common.quotation_subjectEmotionsHappiness050109 social psychologyHealth Informatics02 engineering and technologyModels PsychologicalMachine learningcomputer.software_genrePredictive Value of TestsSurveys and QuestionnairesBayesian multivariate linear regressionAdaptation Psychological0202 electrical engineering electronic engineering information engineeringCIENCIAS DE LA COMPUTACION E INTELIGENCIA ARTIFICIALHumans03.- Garantizar una vida saludable y promover el bienestar para todos y todas en todas las edades0501 psychology and cognitive sciencesDimension (data warehouse)HappinessHappiness-Degree Predictor (H-DP)media_commonMathematicsArtificial neural networkbusiness.industryPsychological researchDeep learning05 social sciencesSocial SupportDeep learningOutcome (probability)Computer Science ApplicationsData-structure driven deep neural network (D-SDNN)Cross-Sectional StudiesMultivariate AnalysisHappinessORGANIZACION DE EMPRESASFemale020201 artificial intelligence & image processingArtificial intelligencePositive psychologybusinessMATEMATICA APLICADAcomputerAlgorithmsMedical InformaticsStress PsychologicalSoftwaredescription
Abstract Background and Objective: Happiness is a universal fundamental human goal. Since the emergence of Positive Psychology, a major focus in psychological research has been to study the role of certain factors in the prediction of happiness. The conventional methodologies are based on linear relationships, such as the commonly used Multivariate Linear Regression (MLR), which may suffer from the lack of representative capacity to the varied psychological features. Using Deep Neural Networks (DNN), we define a Happiness Degree Predictor (H-DP) based on the answers to five psychometric standardized questionnaires. Methods: A Data-Structure driven architecture for DNNs (D-SDNN) is proposed for defining a HDP in which the network architecture enables the conceptual interpretation of psychological factors associated to happiness. Four different neural network configurations have been tested, varying the number of neurons and the presence or absence of bias in the hidden layers. Two metrics for evaluating the influence of conceptual dimensions have been defined and computed: one quantifies the influence weight of the conceptual dimension in absolute terms and the other one pinpoints the direction (positive or negative) of the influence. Materials: A cross-sectional survey targeting non-institutionalized adult population residing in Spain was completed by 823 cases. The total of 111 elements of the survey are grouped by socio-demographic data and by five psychometric scales (Brief COPE Inventory, EPQR-A, GHQ-28, MOS-SSS and SDHS) measuring several psychological factors acting one as the outcome (SDHS) and the four others as predictors. Results: Our D-SDNN approach provided a better outcome (MSE: 1.46 · 10 − 2 ) than MLR (MSE: 2.30 · 10 − 2 ), hence improving by 37% the predictive accuracy, and allowing to simulate the conceptual structure. Conclusions: We observe a better performance of Deep Neural Networks (DNN) with respect to traditional methodologies. This demonstrates its capability to capture the conceptual structure for predicting happiness degree through psychological variables assessed by standardized questionnaires. It also permits to estimate the influence of each factor on the outcome without assuming a linear relationship.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2017-04-28 |