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Volume 19, Issue 1
  • ISSN: 1872-2121
  • E-ISSN: 2212-4047

Abstract

Background

Fetal health monitoring throughout pregnancy is challenging and complex. Complications in the fetal health not identified at the right time lead to mortality of the fetus as well the pregnant women. Hence, obstetricians check the fetal health state by monitoring the fetal heart rate (FHR). Cardiotocography (CTG) is a technique used by obstetricians to access the physical well-being of fetal during pregnancy. It provides information on the fetal heart rate and uterine respiration, which can assist in determining whether the fetus is normal or suspect or pathology. CTG data has typically been evaluated using machine learning (ML) algorithms in predicting the wellness of the fetal and speeding up the detection process.

Methods

In this work, we developed LightGBM with a Grid search-based hyperparameter tuning model to predict fetal health classification. The classification results are analysed quantitatively using the performance measures, namely, precision, Recall, F1-Score, and Accuracy Comparisons were made between different classification models like Logistic Regression, Decision Tree, Random Forest, k-nearest neighbors, Bagging, ADA boosting, XG boosting, and LightGBM, which were trained with the CTG Dataset obtained by the patented fetal monitoring system of 2,216 data points from pregnant women in their third trimester available in the Kaggle dataset. The dataset contains three classes: normal, suspect, and pathology. Our proposed model will give better results in predicting fetal health classification.

Results

In this paper, the performance of the proposed algorithm LightGBM is compared and experimented with various Machine learning Techniques namely LR, DT, RF, KNN, Boosting, Ada boosting, and XG Boost and the classification accuracy of the respective algorithms are 84%, 94%, 93%, 88%, 94%, 89%, 96%. The LightGBM achieved a performance of 97% and outperforms the former models.

Conclusion

The LightGBM-based fetal health classification has been presented. Ensemble models were applied to the FHR dataset and presented the hybrid algorithm, namely Light GBM, and its application to fetal health classification. LightGBM has advantages that include fast training, improved performance, scale-up capabilities, and lesser memory usage than other ensemble models. The proposed model is more consistent and superior to other considered machine learning models and is suitable for the classification of fetal health based on FHR data. Finally, the outcomes of the multiple methods are compared using the same training and test data in order to verify the efficiency of LightGBM. The model can be further enhanced by making it hybrid by combining the advantages of different models and optimization techniques.

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2025-01-01
2024-11-22

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References

  1. GillH.S. KhehraB.S. Fruit image classification using deep learning Comput Mater Cont., 713513551502022202210.21203/rs.3.rs‑574901/v1
     [Google Scholar]
  2. GillH.S. KhalafO.I. AlotaibiY. AlghamdiS. AlasseryF. Multi-model CNN-RNN-LSTM based fruit recognition and classification.Intel Auto Soft Comput.2022331637650
     [Google Scholar]
  3. RajalakshmiM. SaravananV. ArunprasadV. RomeroC.T. KhalafO.I. KarthikC. Machine Learning for modeling and control of industrial clarifier process.Intel Auto Soft Computing.2022321339359
     [Google Scholar]
  4. SurendranR. Ibrahim KhalafO. Andres Tavera RomeroC. Deep learning based intelligent industrial fault diagnosis model. CMC-Computers.Comput. Mater. Continua20227036323633810.32604/cmc.2022.021716
     [Google Scholar]
  5. SpilkaJ. FreconJ. LeonarduzziR. PustelnikN. AbryP. DoretM. Sparse support vector machine for intrapartum fetal heart rate classification.IEEE J. Biomed. Health Inform.201721366467110.1109/JBHI.2016.2546312 27046884
     [Google Scholar]
  6. PonsiglioneA.M. CosentinoC. CesarelliG. AmatoF. RomanoM. A comprehensive review of techniques for processing and analyzing fetal heart rate signals.Sensors 20212118613610.3390/s21186136 34577342
     [Google Scholar]
  7. LiuL. JiaoY. LiX. Machine learning algorithms to predict early pregnancy loss after in vitro fertilization-embryo transfer with fetal heart rate as a strong predictor.Comput. Methods Programs Biomed.202019610562410.1016/j.cmpb.2020.105624
     [Google Scholar]
  8. OgunyemiD. JovanovskiA. FriedmanP. SweatmanB. MadanI. Temporal and quantitative associations of electronic fetal heart rate monitoring patterns and neonatal outcomes †.J. Matern. Fetal Neonatal Med.201932183115312410.1080/14767058.2018.1456523 29621921
     [Google Scholar]
  9. KongY. XuB. ZhaoB. Deep gaussian mixture model on multiple interpretable features of fetal heart rate for pregnancy wellness.ChamSpringer202110.1007/978‑3‑030‑75762‑5_20
     [Google Scholar]
  10. E. AVUÇLU EL. Abdullah, “Classification of Cardiotocography Records with Naïve Bayes.Int. Scientific Voca. Studies J.202032105110
     [Google Scholar]
  11. HaqueE. GuptaT. SinghV. Detection and classification of fetal heart rate (fhr).", In: International Conference on Artificial Intelligence and Sustainable Engineering.Springer202243744710.1007/978‑981‑16‑8542‑2_35
     [Google Scholar]
  12. C¨omertZ. BoopathiA.M. VelappanS. The influences of different window functions and lengths on image-based timefrequency features of fetal heart rate signals 2018 26th Signal Processing and Communications Applications Conference (SIU). 2018 Izmir, Turkey, May 2-5,20131410.1109/SIU.2018.8404247
     [Google Scholar]
  13. ChauhanV.K. DahiyaK. SharmaA. Problem formulations and solvers in linear SVM: a review.Artif. Intell. Rev.201952280385510.1007/s10462‑018‑9614‑6
     [Google Scholar]
  14. ArifM.Z. AhmedR. SadiaU.H. TultulM.S.I. ChakmaR. Decision tree method using for fetal state classification from cardiotography data.J. Adv. Engin. Comput.202041647310.25073/jaec.202041.273
     [Google Scholar]
  15. RamlaM. SangeethaS. NickolasS. Fetal health state monitoring using decision tree classifier from cardiotocography measurements 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS)., 2018 Madurai, India, Jun 14-15, 2018.10.1109/ICCONS.2018.8663047
     [Google Scholar]
  16. KumarG.R. SheshannaK.V. BashaS.R. An improved decision tree classification approach for expectation of cardiotocogram.In: Proceedings of International Conference on Computational Intelligence, Data Science and Cloud Computing.ChamSpringer202132733310.1007/978‑981‑33‑4968‑1_26
     [Google Scholar]
  17. KuoP.L. YenL.B. DuY.C. ChenP-F. TsaiP-Y. Combination of xgboost analysis and rule-based method for intrapartum cardi-otocograph classification.J. Med. Biol. Eng.202141453454210.1007/s40846‑021‑00642‑y
     [Google Scholar]
  18. LuY. FuX. ChenF. Prediction of fetal weight at varying gesta- tional age in the absence of ultrasound examination using ensemble learning.Artif. Intell. med.2020102748
     [Google Scholar]
  19. Fetal health classification., 20212021Available from: https://www.kaggle.com/datasets/andrewmvd/fetal-healthclassification [accessed on: 22-12-2021]
  20. Ayres-de CamposD. BernardesJ. GarridoA. Marques-de-SáJ. Pereira-LeiteL. SisPorto 2.0: a program for automated analysis of cardiotocograms.J. Matern. Fetal Med.20009531131810.1002/1520‑6661(200009/10)9:5<311::AIDMFM12>3.0.CO2‑9 11132590
     [Google Scholar]
  21. PedregosaF. VaroquauxG. GramfortA. Machine learning in python.J. machine. Learn. res.20111228252830
     [Google Scholar]
  22. GatY. Fetal monitoring system and methodU.S. Patent 5,954,663, 2011.
     [Google Scholar]
  23. JiangN. FuF. ZuoH. ZhengX. ZhengQ. A Municipal PM2. 5 Forecasting Method Based on Random Forest and WRF Model.Engin. Lett.2020282
     [Google Scholar]
  24. ZhangQ. YangM. KpalmaK. ZhengQ. ZhangX. Segmentation of hand posture against complex backgrounds based on saliency and skin colour detection.IAENG Int. J. Comput. Sci.2018453435444
     [Google Scholar]
  25. AbuelezzI. HassanA. JaberB.A. ShariqueM. Abd-AlrazaqA. HousehM. AlamT. ShahZ. Contribution of Artificial Intelligence in Pregnancy: A Scoping Review.Stud. Health Technol. Inform.202228933333610.3233/SHTI210927 35062160
     [Google Scholar]
  26. AmigoJ.M. Data mining, machine learning, deep learning, chemometrics: Definitions, common points and trends (Spoiler Alert: VALIDATE your models!).Braz. J. Analytical Chem.2021832456110.30744/brjac.2179‑3425.AR‑38‑2021
     [Google Scholar]
  27. AlamM.T. KhanM.A.I. DolaN.N. TazinT. KhanM.M. AlbraikanA.A. AlmalkiF.A. Comparative analysis of different efficient machine learning methods for fetal health classification.Appl. Bionics Biomech.2022202211210.1155/2022/6321884 35498140
     [Google Scholar]
  28. AkhtarF. LiJ. AzeemM. ChenS. PanH. WangQ. YangJ-J. Effective large for gestational age prediction using machine learning techniques with monitoring biochemical indicators.J. Supercomput.20207686219623710.1007/s11227‑018‑02738‑w
     [Google Scholar]
  29. MehbodniyaA. LazarAJ.P. WebberJ. Fetal health classifica- tion from cardiotocographic data using machine learning.Expert Systems.2022396899
     [Google Scholar]
  30. ChenY. GuoA. ChenQ. Intelligent classification of antepartum cardiotocography model based on deep forest.Biomedical. Signal Processing and. Control20216710255510.1016/j.bspc.2021.102555
     [Google Scholar]
  31. PrasetyoS.E. PrastyoP.H. ArtiS. A cardiotocographic classification using feature selection: A comparative study.J. Inform. Technol. Comp. Engin.202151253210.25077/jitce.5.01.25‑32.2021
     [Google Scholar]
  32. AgrawalK. MohanH. Cardiotocography analysis for fetal state classifi-cation using machine learning algorithms2019 International Conference on Computer Communication and Informatics (ICCCI) 2019 Jan. 23 – 25, Coimbatore, INDIA 201916
     [Google Scholar]
  33. Imran MollaM. JuiJ.J. BariB.S. Cardiotocogram data classifica- tion using random forest based machine learning algorithmProceedings of the 11th National Technical Seminar on Unmanned System Technology, 2021
     [Google Scholar]
  34. AfridiR. IqbalZ. KhanM. AhmadA. NaseemR. Fetal heart rate classification and comparative analysis using cardiotocography data and known classifiers.Int. J. Grid Distrib. Comput.2019121314210.33832/ijgdc.2019.12.1.03
     [Google Scholar]
  35. IslamS.F.N. YulitaI.N. Predicting fetal condition from cardiotocography results using the random forest method", Proceedings of the 7th Mathematics, Science, and Computer Science Education International Seminar, 2020 Oct 12, 2019, Bandung, West Java, Indonesia,229654010.4108/eai.12‑10‑2019.2296540
     [Google Scholar]
  36. RamlaM. SangeethaS. NickolasS. Fetal health state monitoring using decision tree classifier from cardiotocography measurements Madurai, India, Jun 14-15, 2018, pp. 1799-1803,10.1109/ICCONS.2018.8663047
     [Google Scholar]
  37. SontakkeS.A. LohokareJ. DaniR. Classification of cardiotocog- raphy signals using machine learningProceedings of SAI Intelligent Systems Conference Springer: Cham2018439450
     [Google Scholar]
  38. ShahS.A.A. AzizW. ArifM. Decision trees based classification of cardiotocograms using bagging approach 2015 13th International Conference on Frontiers of Information Technology (FIT),, 2015 Islamabad, Pakistan, Dec 14-16, 2015121710.1109/FIT.2015.14
     [Google Scholar]
  39. BhowmikP. BhowmikPC. AliUME. Cardiotocography data analysis to predict fetal health risks with tree-based ensemble learningInt J Inform Technol Comput Sci.,13530402021202110.5815/ijitcs.2021.05.03
     [Google Scholar]
  40. RafieA. ChenouniS. AlamiN. Classification of fetal state using machine learning models. E3S Web of Conferences.EDP Sciences20220102710.1051/e3sconf/202235101027
     [Google Scholar]
/content/journals/eng/10.2174/1872212118666230703155834
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Fetal Health Classification using LightGBM with Grid Search Based Hyper Parameter Tuning
Recent Pat Eng 19, E030723218386 (2025); https://doi.org/10.2174/1872212118666230703155834
/content/journals/eng/10.2174/1872212118666230703155834
/content/journals/eng/10.2174/1872212118666230703155834
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  • Article Type:     Research Article
Keyword(s): ensemble model; extreme gradient boosting; Fetal health classification; grid search; LightGBM; logistic regression

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