Title: Uniform distribution tuna swarm optimisation and deep neural network for foetal health classification

Authors: B. Jansi; V. Sumalatha

Addresses: Department of Computer Science, Vels Institute of Science, Technology and Advanced Studies (VISTAS), Pallavaram, Chennai, Tamil Nadu, India ' Department of Computer Science, Vels Institute of Science, Technology and Advanced Studies (VISTAS), Pallavaram, Chennai, Tamil Nadu, India

Abstract: Foetal health is generally accessed by foetal heart rate (FHR) monitoring throughout the antepartum period. FHR analysis is a difficult and illogical process because of restricted dependability. Previous research only examined cardiotocographic (CTG) dataset classification accuracy, ignoring computational time, a critical clinical issue. Using uniform distribution tuna swarm optimisation (UDTSO), this paper selects the most important CTG traits. This study developed a machine-learning algorithm to differentiate normal and abnormal foetal CTG data. The proposed study involves pre-processing, FS, classification, and outcomes evaluation. The dataset is normalised using min-max normalisation first in pre-processing. Min-max normalisation modifies characteristics from 0 to 1 range. In the second feature selection step, the UDTSO algorithm selects a subset of input characteristics to evaluate accuracy and choose the optimum solution. Third, a deep neural network (DNN) classifies CTG recordings as normal (N), suspect (S), or pathologic (P). DNN's AlexNet-SVM captures convolution layer filter data. Max pooling minimises weights and concatenates output from a collection of neurons. The fully linked layers now have the AlexNet-SVM classifier to reduce time complexity. Classifiers are assessed on precision, recall, f-measure, and accuracy. The CTG dataset comes from UCI Machine Learning Repository.

Keywords: foetal heart rate; FHR; uniform distribution tuna swarm optimisation; UDTSO; deep neural network; DNN; support vector machine; SVM; cardiotocographic; CTG; University of California Irvine; UCI.

DOI: 10.1504/IJBRA.2024.139998

International Journal of Bioinformatics Research and Applications, 2024 Vol.20 No.3, pp.244 - 263

Received: 06 Sep 2023
Accepted: 30 Nov 2023
Published online: 15 Jul 2024 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article