Fetal health status prediction based on maternal clinical history using machine learning techniques - Ertuğrul, Egemen; AKBULUT, AKHAN (Elsevier Ireland Ltd, Elsevier House, Brookvale Plaza, East Park Shannon, Co, Clare, 00000, Ireland, 2018)
Background and Objective: Congenital anomalies are seen at 1-3% of the population, probabilities of which are tried to be found out primarily through double, triple and quad tests during pregnancy. Also, ultra-sonographical evaluations of fetuses enhance detecting and defining these abnormalities. About 60-70% of the anomalies can be diagnosed via ultra-sonography, while the remaining 30-40% can be diagnosed after childbirth. Medical diagnosis and prediction is a topic that is closely related with e-Health and machine learning. e-Health applications are critically important especially for the patients unable to see a doctor or any health professional. Our objective is to help clinicians and families to better predict fetal congenital anomalies besides the traditional pregnancy tests using machine learning techniques and e-Health applications. Methods: In this work, we developed a prediction system with assistive e-Health applications which both the pregnant women and practitioners can make use of. A performance comparison (considering Accuracy, Fl-Score, AUC measures) was made between 9 binary classification models (Averaged Perceptron, Boosted Decision Tree, Bayes Point Machine, Decision Forest, Decision Jungle, Locally-Deep Support Vector Machine, Logistic Regression, Neural Network, Support Vector Machine) which were trained with the clinical dataset of 96 pregnant women and used to process data to predict fetal anomaly status based on the maternal and clinical data. The dataset was obtained through maternal questionnaire and detailed evaluations of 3 clinicians from RadyoEmar radiodiagnostics center in Istanbul, Turkey. Our e-Health applications are used to get pregnant women's health status and clinical history parameters as inputs, recommend them physical activities to perform during pregnancy, and inform the practitioners and finally the patients about possible risks of fetal anomalies as the output. Results: In this paper, the highest accuracy of prediction was displayed as 89.5% during the development tests with Decision Forest model. In real life testing with 16 users, the performance was 87.5%. This estimate is sufficient to give an idea of fetal health before the patient visits the physician. Conclusions: The proposed work aims to provide assistive services to pregnant women and clinicians via an online system consisting of a mobile side for the patients, a web application side for their clinicians and a prediction system. In addition, we showed the impact of certain clinical data parameters of pregnant on the fetal health status, statistically correlated the parameters with the existence of fetal anomalies and showed guidelines for future researches. (C) 2018 Elsevier B.V. All rights reserved.
Keywords: Machine learning; Medical diagnosis; Risk prediction; Pregnancy; Fetal health; Prognosis; m-Health; ROC CURVE; CLASSIFICATION; ALGORITHMS; BRAIN