Fuzzy Logic Prediction of Hypertensive Disorders in Pregnancy Using the Takagi–Sugeno and C-Means Algorithms

Israel Campero-Jurado, Daniel Robles-Camarillo, Jorge A. Ruiz-Vanoye, Juan M. Xicoténcatl-Pérez, Ocotlán Díaz-Parra, Julio-César Salgado-Ramírez, Francisco Marroquín-Gutiérrez and Julio Cesar Ramos-Fernández ()
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Israel Campero-Jurado: Department of Mathematics and Computer Science, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
Daniel Robles-Camarillo: Research, Innovation and Graduate Department, Universidad Politécnica de Pachuca, Carr. Cd. Sahagún-Pachuca Km. 20, Zempoala 43830, Mexico
Jorge A. Ruiz-Vanoye: Research, Innovation and Graduate Department, Universidad Politécnica de Pachuca, Carr. Cd. Sahagún-Pachuca Km. 20, Zempoala 43830, Mexico
Juan M. Xicoténcatl-Pérez: Research, Innovation and Graduate Department, Universidad Politécnica de Pachuca, Carr. Cd. Sahagún-Pachuca Km. 20, Zempoala 43830, Mexico
Ocotlán Díaz-Parra: Research, Innovation and Graduate Department, Universidad Politécnica de Pachuca, Carr. Cd. Sahagún-Pachuca Km. 20, Zempoala 43830, Mexico
Julio-César Salgado-Ramírez: Research, Innovation and Graduate Department, Universidad Politécnica de Pachuca, Carr. Cd. Sahagún-Pachuca Km. 20, Zempoala 43830, Mexico
Francisco Marroquín-Gutiérrez: Research, Innovation and Graduate Department, Universidad Politécnica de Pachuca, Carr. Cd. Sahagún-Pachuca Km. 20, Zempoala 43830, Mexico
Julio Cesar Ramos-Fernández: Research, Innovation and Graduate Department, Universidad Politécnica de Pachuca, Carr. Cd. Sahagún-Pachuca Km. 20, Zempoala 43830, Mexico

Mathematics, 2024, vol. 12, issue 15, 1-18

Abstract: Hypertensive disorders in pregnancy, which include preeclampsia, eclampsia, and chronic hypertension, complicate approximately 10% of all pregnancies in the world, constituting one of the most serious causes of mortality and morbidity in gestation. To help predict the occurrence of hypertensive disorders, a study based on algorithms that help model this health problem using mathematical tools is proposed. This study proposes a fuzzy c-means (FCM) model based on the Takagi–Sugeno (T-S) type of fuzzy rule to predict hypertensive disorders in pregnancy. To test different modeling methodologies, cross-validation comparisons were made between random forest, decision tree, support vector machine, and T-S and FCM methods, which achieved 80.00%, 66.25%, 70.00%, and 90.00%, respectively. The evaluation consisted of calculating the true positive rate (TPR) over the true negative rate (TNR), with equal error rate (EER) curves achieving a percentage of 20%. The learning dataset consisted of a total of 371 pregnant women, of which 13.2% were diagnosed with a condition related to gestational hypertension. The dataset for this study was obtained from the Secretaría de Salud del Estado de Hidalgo (SSEH), México. A random sub-sampling technique was used to adjust the class distribution of the data set, and to eliminate the problem of unbalanced classes. The models were trained using a total of 98 samples. The modeling results indicate that the T-S and FCM method has a higher predictive ability than the other three models in this research.

Keywords: modeling hypertension in pregnancy; fuzzy c-means; dimension reduction; Takagi–Sugeno (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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