Uniform distribution tuna swarm optimisation and deep neural network for foetal health classification
by B. Jansi; V. Sumalatha
International Journal of Bioinformatics Research and Applications (IJBRA), Vol. 20, No. 3, 2024

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.

Online publication date: Mon, 15-Jul-2024

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