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

作　　者：Uddagiri Sirisha Parvathaneni Naga Srinivasu Panguluri Padmavathi Seongki Kim Aruna Pavate Jana Shafi Muhammad Fazal Ijaz 

机构地区：[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 inWadi Alddawasir,Prince Sattam bin Abdulaziz University,Wadi Alddawasir,11991,Saudi Arabia [8]School of IT and Engineering,Melbourne Institute of Technology,Melbourne,3000,Australia

出　　处：《Computers, Materials & Continua》2024年第8期2301-2330,共30页计算机、材料和连续体（英文）

基　　金：This work was supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(NRF-2023R1A2C1005950);Jana Shafi is supported via funding from Prince Sattam bin Abdulaziz University Project Number(PSAU/2024/R/1445).

摘　　要：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.

关 键 词：Fetal health cardiotocography data deep learning dynamic multi-layer perceptron feature engineering 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]