Fuzzy Inference System for Risk Evaluation in Gestational Diabetes Mellitus

Remote monitoring health data analysis holds the potential to reduce pregnancy complications, improve patients' quality of life, enhance the efficiency of healthcare delivery and reduce healthcare costs. In this paper, we present a method based on fuzzy inference systems to monitor pregnancies complicated by gestational diabetes mellitus (GDM). The system is simple, fast, flexible and exploits domain expertise in assessing risk levels according to capillary glucose levels from women with GDM. We show that this approach generates an interpretable input, which is valuable in medical applications. To prove the capabilities of the system, we present prediction results from 50 real-world patients and show that the system obtains relevant glycaemic-control data comparable to current monitoring methods that rely on periodic face-to-face physician review. Our systems achieves 95% accuracy. Moreover, we show that the difference in predictions account for a more personalized treatment.