Comparison of machine learning algorithms to classify fetal health using cardiotocogram data

Nabillah Rahmayanti, Humaira Pradani, Muhammad Pahlawan, Retno Vinarti*

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

24 Citations (Scopus)


Cardiotocogram (CTG) is one of the monitoring tools to estimate the fetus health in womb. CTG mainly yields two results fetal health rate (FHR) and uterine contractions (UC). In total, there are 21 attributes in the measurement of FHR and UC on CTG. These attributes can help obstreticians to clasify whether the fetus health is normal, suspected, or pathological. This paper compares 7 algorithms to predict the fetus health: Artificial Neural Network (ANN), Long-short Term Memory (LSTM), XG Boost (XGB), Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Light GBM (LGBM), and Random Forest (RF). By employing three scenarios, this paper reports the performance measurements among those algorithms. The results show that 5 out of 7 algorithms perform very well (89-99% accurate). Those five algorithms are XGB, SVM, KNN, LGBM, RF. Furthermore, only one from five algorithm that always performs well through three scenarios: LGBM.

Original languageEnglish
Pages (from-to)162-171
Number of pages10
JournalProcedia Computer Science
Publication statusPublished - 2021
Event6th Information Systems International Conference, ISICO 2021 - Virtual, Online, Italy
Duration: 7 Aug 20218 Aug 2021


  • Cardiotocogram
  • Classification
  • Fetal health
  • Machine learning


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