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Review
. 2025 Apr 11;15(8):978.
doi: 10.3390/diagnostics15080978.

A Review of the Use of Data Analytics to Address Preeclampsia in Ecuador Between 2020 and 2024

Franklin Parrales-Bravo  1   2 , Lorenzo Cevallos-Torres  1   2 , Leonel Vasquez-Cevallos  3 , Rosangela Caicedo-Quiroz  2 , Roberto Tolozano-Benites  2 , Víctor Gómez-Rodríguez  4
Affiliations
Review

A Review of the Use of Data Analytics to Address Preeclampsia in Ecuador Between 2020 and 2024

Franklin Parrales-Bravo et al. Diagnostics (Basel). .

Abstract

Preeclampsia is one of the leading causes of maternal and perinatal morbidity and mortality worldwide. The incidence of preeclampsia in Ecuador is approximately 51 cases per 1000 pregnancies. Despite advances in medicine, its diagnosis and management remain a challenge due to its multifactorial nature and variability in its clinical presentation. Data analytics offers an innovative approach to address these challenges, allowing for better understanding of the disease and more informed decision-making. This work review examines peer-reviewed studies published during the last decade that employed descriptive, diagnostic, predictive, and prescriptive analytics to evaluate preeclampsia in Ecuador. The review focuses on studies conducted in healthcare institutions across coastal and highland regions, with an inclusion criterion requiring sample sizes greater than 100 patients. Emphasis is placed on the statistical methods used, main findings, and the technological capabilities of the facilities where the analyses were performed. Critical evaluation of methodology limitations and a comparative discussion of findings with global literature on preeclampsia are included. The synthesis of these studies highlights both progress and gaps in predictive analytics for preeclampsia and suggests pathways for future research.

Keywords: clinical decision support systems; data analytics; descriptive; diagnostic; disease diagnosis; predictive; preeclampsia; prescriptive.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Data analytics approaches, value, and complexity.
Figure 2
Figure 2
Flowchart of study selection according to the PRISMA guidelines.
Figure 3
Figure 3
Descriptive techniques.
Figure 4
Figure 4
Diagnostic techniques.
Figure 5
Figure 5
Predictive techniques.
See this image and copyright information in PMC

References

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    1. Parrales-Bravo F., Saltos-Cedeño J., Tomalá-Esparza J., Barzola-Monteses J. Clustering-based Approach for Characterization of Patients with Preeclampsia using a Non-Redundant Feature Selection; Proceedings of the 2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), Tenerife; Canary Islands, Spain. 19–21 July 2023; pp. 1–6.
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