Cardiotocographic Diagnosis of Fetal Health based on Multiclass Morphologic Pattern Predictions using Deep Learning Classification

Abstract

Abstract-Medical complications of pregnancy and pregnancy-related deaths continue to remain a major global challenge today. Internationally, about 830 maternal deaths occur every day due to pregnancy-related or childbirth-related complications. In fact, almost 99% of all maternal deaths occur in developing countries. In this research, an alternative and enhanced artificial intelligence approach is proposed for cardiotocographic diagnosis of fetal assessment based on multiclass morphologic pattern predictions, including 10 target classes with imbalanced samples, using deep learning classification models. The developed model is used to distinguish and classify the presence or absence of multiclass morphologic patterns for outcome predictions of complications during pregnancy. The testing results showed that the developed deep neural network model achieved an accuracy of 88.02%, a recall of 84.30%, a precision of 85.01%, and an F-score of 0.8508 in average. Thus, the developed model can provide highly accurate and consistent diagnoses for fetal assessment regarding complications during pregnancy, thereby preventing and/or reducing fetal mortality rate as well as maternal mortality rate during and following pregnancy and childbirth, especially in lowresource settings and developing countries.

Towards deep phenotyping pregnancy: a systematic review on artificial intelligence and machine learning methods to improve pregnancy outcomes

“…Unsupervised learning techniques were applied: independent component analysis [ 78 , 83 , 85 , 86 ], principle component analysis [ 46 , 91 ] and Kalman filtering [ 82 ]. Moreover, studies applied a variety of ML methods to classify CTG signals and determine fetal state, with best performances demonstrated using SVM [ 87 , 89 ], DL [ 90 ] and RF [ 88 ].…”

Prediction of Recombination Spots Using Novel Hybrid Feature Extraction Method via Deep Learning Approach

“…Deep learning algorithms apply neural networks that learn features from the data directly and then make decisions. Recently, deep learning algorithms received significant attention in the field of bioinformatics and computational biology (Lecun et al, 2015 ; Angermueller et al, 2016 ; Kelley et al, 2016 ; Mamoshina et al, 2016 ; Quang and Xie, 2016 ; Min et al, 2017 ; Cohn et al, 2018 ; Miao and Miao, 2018 ; Telenti et al, 2018 ; Zhang et al, 2019 ). A DNN model comprises an input layer, output layer, and multiple hidden layers, as shown in Figure 2 .…”

iEnhancer-DHF: Identification of Enhancers and Their Strengths Using Optimize Deep Neural Network With Multiple Features Extraction Methods

“…It consist of multi-layers having a various number of neurons that actively participating in learning process. The given sequences in the network are represented in the form of hierarchal structure with increasing levels of abstraction [53]. The neurons in the network are configured in the form of linear and non-linear activation function.…”

“…It has been shown that the deep algorithms employed successfully in a number of fields such as image recognition [55]- [57], speech recognition [58], [59], natural language processing [60], [61] and bioinformatics [62]- [64]. Additionally, it has been presented by several researchers that the DNN demonstrated superior performance over the traditional learning approaches employed for a various complex problems [53] [65]. Due to a successful implementation of the DNN in various field for the complex classifications, this paper employed the DNN as a predictor.…”