Postpartum depression prediction through pregnancy data analysis for emotion-aware smart systems
| Autor: | Igor V. Illin, Kashif Saleem, Neeraj Kumar, Joel J. P. C. Rodrigues, Mario W. L. Moreira |
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| Rok vydání: | 2019 |
| Předmět: |
Postpartum depression
Pregnancy Smart system Computer science business.industry Process (engineering) Behavior change Big data Improved algorithm 020206 networking & telecommunications Cloud computing 02 engineering and technology medicine.disease Data science Hardware and Architecture Signal Processing 0202 electrical engineering electronic engineering information engineering medicine 020201 artificial intelligence & image processing business Software Information Systems |
| Zdroj: | Information Fusion. 47:23-31 |
| ISSN: | 1566-2535 |
| DOI: | 10.1016/j.inffus.2018.07.001 |
| Popis: | Emotion-aware computing represents an evolution in machine learning enabling systems and devices process to interpret emotional data to recognize human behavior changes. As emotion-aware smart systems evolve, there is an enormous potential for increasing the use of specialized devices that can anticipate life-threatening conditions facilitating an early response model for health complications. At the same time, applications developed for diagnostic and therapy services can support conditions recognition (as depression, for instance). Hence, this paper proposes an improved algorithm for emotion-aware smart systems, capable for predicting the risk of postpartum depression in women suffering from hypertensive disorders during pregnancy through biomedical and sociodemographic data analysis. Results show that ensemble classifiers represent a leading solution concerning predicting psychological disorders related to pregnancy. Merging novel technologies based on IoT, cloud computing, and big data analytics represent a considerable advance in monitoring complex diseases for emotion-aware computing, such as postpartum depression. |
| Databáze: | OpenAIRE |
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