Fuzzy Intelligent System for Patients with Preeclampsia in Wearable Devices
| Autor: | Javier Medina, Jorge Londoño, Sixto Campaña, Á.L. García-Fernández, Macarena Espinilla |
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| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
Article Subject
Computer Networks and Communications Computer science Decision tree Wearable computer 02 engineering and technology TK5101-6720 computer.software_genre Fuzzy logic 03 medical and health sciences 0302 clinical medicine Knowledge extraction 0202 electrical engineering electronic engineering information engineering Wearable technology Interpretability Flexibility (engineering) 030219 obstetrics & reproductive medicine business.industry 3. Good health Computer Science Applications Telecommunication 020201 artificial intelligence & image processing Data mining business computer Decision analysis |
| Zdroj: | Mobile Information Systems, Vol 2017 (2017) Mobile Information Systems |
| Popis: | Preeclampsia affects from 5% to 14% of all pregnant women and is responsible for about 14% of maternal deaths per year in the world. This paper is focused on the use of a decision analysis tool for the early detection of preeclampsia in women at risk. This tool applies a fuzzy linguistic approach implemented in a wearable device. In order to develop this tool, a real dataset containing data of pregnant women with high risk of preeclampsia from a health center has been analyzed, and a fuzzy linguistic methodology with two main phases is used. Firstly, linguistic transformation is applied to the dataset to increase the interpretability and flexibility in the analysis of preeclampsia. Secondly, knowledge extraction is done by means of inferring rules using decision trees to classify the dataset. The obtained linguistic rules provide understandable monitoring of preeclampsia based on wearable applications and devices. Furthermore, this paper not only introduces the proposed methodology, but also presents a wearable application prototype which applies the rules inferred from the fuzzy decision tree to detect preeclampsia in women at risk. The proposed methodology and the developed wearable application can be easily adapted to other contexts such as diabetes or hypertension. |
| Databáze: | OpenAIRE |
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