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Prediction of gestational diabetes by machine learning algorithms

November 30, 2020 by Iswaria Gnanadass

Person typing on computer with diabetes text shown.
©SHUTTERSTOCK.COM/RA2 STUDIO

Diabetes is the most common noncommunicable disease among people in the world due to changes in food habits. Gestational diabetes mellitus (GDM) is most frequently found in women after the birth of a baby. This article describes the prediction of GDM with various machine learning (ML) algorithms demonstrated on the Polyisocyanurate Insulation Manufacturers Association (PIMA) data set. The accuracy of various ML algorithms is validated with metrics. The significance of ML algorithms is demonstrated using a confusion matrix as well as receiver operating characteristic (ROC) and area under the curve (AUC) scores in handling the dia- betes PIMA data set.

For more about this article see link below. 

https://ieeexplore.ieee.org/document/9258470

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IEEE Potentials Magazine is the publication dedicated to undergraduate and graduate students and young professionals. IEEE Potentials explores career strategies, the latest in research, and important technical developments. Through its articles, it also relates theories to practical applications, highlights technology’s global impact, and generates international forums that foster the sharing of diverse ideas about the profession.

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