Open Access

ARTICLE

Dynamic Multi-Layer Perceptron for Fetal Health Classification Using Cardiotocography Data

by Uddagiri Sirisha^1,#, Parvathaneni Naga Srinivasu^2,3,*, Panguluri Padmavathi⁴, Seongki Kim^5,#, Aruna Pavate⁶, Jana Shafi⁷, Muhammad Fazal Ijaz^8,*

1 Department of Computer Science and Engineering, Prasad V Potluri Siddhartha Institute of Technology, Vijayawada, 520007, India
2 Amrita School of Computing, Amrita Vishwa Vidyapeetham, Amaravati, Andhra Pradesh, 522503, India
3 Department of Tele informatics Engineering, Federal University of Ceará, Fortaleza, 60455-970, Brazil
4 Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, 522302, India
5 Department of Computer Engineering, Chosun University, Gwangju, 61452, Republic of Korea
6 School of CSIT, Symbiosis Skills and Professional University, Pune, 412101, India
7 Department of Computer Engineering and Information, College of Engineering in Wadi Alddawasir, Prince Sattam bin Abdulaziz University, Wadi Alddawasir, 11991, Saudi Arabia
8 School of IT and Engineering, Melbourne Institute of Technology, Melbourne, 3000, Australia

* Corresponding Authors: Parvathaneni Naga Srinivasu. Email: ; Muhammad Fazal Ijaz. Email:

(This article belongs to the Special Issue: Deep Learning and IoT for Smart Healthcare)

Computers, Materials & Continua 2024, 80(2), 2301-2330. https://doi.org/10.32604/cmc.2024.053132

Received 25 April 2024; Accepted 02 July 2024; Issue published 15 August 2024

Abstract

Fetal health care is vital in ensuring the health of pregnant women and the fetus. Regular check-ups need to be taken by the mother to determine the status of the fetus’ growth and identify any potential problems. To know the status of the fetus, doctors monitor blood reports, Ultrasounds, cardiotocography (CTG) data, etc. Still, in this research, we have considered CTG data, which provides information on heart rate and uterine contractions during pregnancy. Several researchers have proposed various methods for classifying the status of fetus growth. Manual processing of CTG data is time-consuming and unreliable. So, automated tools should be used to classify fetal health. This study proposes a novel neural network-based architecture, the Dynamic Multi-Layer Perceptron model, evaluated from a single layer to several layers to classify fetal health. Various strategies were applied, including pre-processing data using techniques like Balancing, Scaling, Normalization hyperparameter tuning, batch normalization, early stopping, etc., to enhance the model’s performance. A comparative analysis of the proposed method is done against the traditional machine learning models to showcase its accuracy (97%). An ablation study without any pre-processing techniques is also illustrated. This study easily provides valuable interpretations for healthcare professionals in the decision-making process.

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