Weighted Deep Neural Network Based Clinical Decision Support System for the Determination of Fetal Health

Abstract

Healthcare industry is undergoing changes at a tremendous rate due to healthcare innovations. Predictive analytics is increasingly being used to diagnose the patient’s ailments and provide actionable insights into already existing healthcare data. The paper looks at a decision support system for determining the health status of the foetus from cardiotographic data using deep learning neural networks. The foetal health records are classified as normal, suspect and pathological. As the multiclass cardiotographic datset of the foetus shows a high degree of imbalance a weighted deep neural network is applied. To overcome the accuracy paradox due to the multiclass imbalance, relevant metrics such as the sensitivity, specificity, F1 Score and Gmean are used to measure the performance of the classifier rather than accuracy. The metrics are applied to the individual classes to ensure that the positive cases are identified correctly. The weighted DNN based classifier is able to classify the positive instances with Gmean score of 91% which is better than than the SVM classifier.

Early Diagnosis and Classification of Fetal Health Status from a Fetal Cardiotocography Dataset Using Ensemble Learning

“…He observed that the SVM model outperformed the others with an F1 score of 93% and an accuracy of 81%. He also reported that the DNN model had higher performance than SVM, with a G-mean of 91% and a sensitivity of 89% [38].…”

Artificial Intelligence in Fetal Health Diagnosis: A Systematic Literature Review

“…Vani conducted an experiment on a decision support system using DL-based neural networks to determine the health status of fetuses from the CTG dataset (86). They used the CTG dataset with 21 features and 2126 examples and used SVM and DNN models with a ratio of 0.7:0.3 for training and testing.…”