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Journal of Computer Sciences and Applications. 2018, 6(2), 82-90
DOI: 10.12691/jcsa-6-2-5
Open AccessArticle

Improved Early Detection of Gestational Diabetes via Intelligent Classification Models: A Case of the Niger Delta Region in Nigeria

A.A. Ojugo1, and D. Otakore1

1Department of Mathematics/Computer, Federal University of Petroleum Resources Effurun, Nigeria

Pub. Date: September 05, 2018

Cite this paper:
A.A. Ojugo and D. Otakore. Improved Early Detection of Gestational Diabetes via Intelligent Classification Models: A Case of the Niger Delta Region in Nigeria. Journal of Computer Sciences and Applications. 2018; 6(2):82-90. doi: 10.12691/jcsa-6-2-5


Diabetes is the most common endocrine-metabolic disorder that features a body characterized by hyperglycaemia – giving rise to risk of microvascular (retinopathy, neupathy and nephropathy) and microvascular (vascular disease, stroke and ischemic heart diseases). Nigeria has become aware of inherent threats of the Type-II diabetes and the consequent metamorphism into gestational diabetes in mothers with or without previous cases of Type-II. We presents a comparative study of classification models using both the supervised and unsupervised evolutionary models. We aim at improved early detection of the disorder via data-mining tools. Adopted dataset is from College of Health and Teaching Hospitals with selected Universities in Niger Delta. Results show that age, body mass index, family ties to second degree, environmental conditions of inhabitance among others are critical factors that increases its likelihood. Gestational diabetes in mothers were confirmed if: (a) history of babies weighing > 4.5kg at birth, (b) insulin resistance with polycystic ovary syndrome, and (c) abnormal tolerance to insulin.

Diabetes microvascular macrovascular fuzzy classifier DiabCare Gestational hyperglycaemia

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