Journal
INFORMATION FUSION
Volume 47, Issue -, Pages 23-31Publisher
ELSEVIER
DOI: 10.1016/j.inffus.2018.07.001
Keywords
Emotion-aware smart systems; Cognitive computing; Information fusion; Mental health solutions; Maternal care quality improvement
Funding
- FCT - Fundacao para a Ciencia e a Tecnologia [UID/EEA/50008/2013]
- Brazilian National Council for Research and Development (CNPq) [309335/2017 - 5]
- Finep
- Funttel under the Centro de Referencia em Radiocomunicacoes - CAR project of the Instituto Nacional de Telecomunicacoes (Inatel), Brazil [01.14.0231.00]
- Ciencia sem Fronteiras of CNPq, Brazil [207706/2014 - 0]
- Deanship of Scientific Research at King Saud University [RG-1439 - 022]
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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.
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