TY - JOUR
T1 - Comparison of machine learning algorithms to classify fetal health using cardiotocogram data
AU - Rahmayanti, Nabillah
AU - Pradani, Humaira
AU - Pahlawan, Muhammad
AU - Vinarti, Retno
N1 - Publisher Copyright:
© 2021 The Authors. Published by Elsevier B.V.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Cardiotocogram
KW - Classification
KW - Fetal health
KW - Machine learning
UR - http://www.scopus.com/inward/record.url?scp=85123784427&partnerID=8YFLogxK
U2 - 10.1016/j.procs.2021.12.130
DO - 10.1016/j.procs.2021.12.130
M3 - Conference article
AN - SCOPUS:85123784427
SN - 1877-0509
VL - 197
SP - 162
EP - 171
JO - Procedia Computer Science
JF - Procedia Computer Science
T2 - 6th Information Systems International Conference, ISICO 2021
Y2 - 7 August 2021 through 8 August 2021
ER -