FETAL HEALTH RISK STATUS IDENTIFICATION SYSTEM BASED ON CARDIOTOCOGRAPHY DATA USING EXTREME GRADIENT BOOSTING WITH ISOLATION FOREST AS OUTLIER DETECTION

  • Firda Yunita Sari Department of Mathematics, Faculty of Science and Technology, UIN Sunan Ampel Surabaya, Indonesia https://orcid.org/0009-0000-5070-5306
  • Dian Candra Rini Novitasari Department of Mathematics, Faculty of Science and Technology, UIN Sunan Ampel Surabaya, Indonesia https://orcid.org/0000-0003-1593-6808
  • Abdulloh Hamid Department of Mathematics, Faculty of Science and Technology, UIN Sunan Ampel Surabaya, Indonesia https://orcid.org/0000-0001-8842-2037
  • Dina Zatusiva Haq Department of Mathematics, Faculty of Science and Technology, UIN Sunan Ampel Surabaya, Indonesia https://orcid.org/0009-0001-8842-2827
DOI: https://doi.org/10.30598/barekengvol19iss3pp1711-1724
Keywords: Classification, Extreme Gradient Boosting (XGBoost), Fetal Health, Isolation Forest (IForest), Outlier Detection

Abstract

Premature birth and birth defects contribute significantly to infant mortality, highlighting the need for early identification of fetal health risks. This study uses XGBoost for fetal health classification, integrating IForest for outlier detection to improve model performance. By varying the contamination percentage, learning rate (η), maximum depth, and n_estimator, the best results were achieved at CP = 8%, η = 0.01, max_depth = 7, and n_estimator = 100, which resulted in 100% accuracy, sensitivity, and specificity with a calculation time of 0.36 seconds. IForest effectively reduced the dataset from 2126 to 1956 samples by removing outliers, improving accuracy by 3.76%, and reducing computation time by 0.51 seconds. These findings suggest that IForest improves classification efficiency while maintaining high predictive performance, supporting early identification of fetal health risks to aid timely medical intervention.

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References

World Health Organization, “PRETERM BIRTH,” 2023. http://www.who.int/en/news-room/fact-sheets/detail/preterm-birth (accessed Dec. 13, 2024).

World Health Organization, “WORLD BIRTH DEFECTS DAY: EVERY JOURNEY MATTERS,” 2024. https://www.who.int/southeastasia/news/detail/02-03-2024-world-birth-defects-day-every-journey-matters#:~:text=Between 2000 and 2021%2C the,for 11%25 of the total. (accessed Dec. 13, 2024).

M. Murugappan, N. B. Prakash, R. Jeya, A. Mohanarathinam, G. R. Hemalakshmi, and M. Mahmud, “A NOVEL FEW-SHOT CLASSIFICATION FRAMEWORK FOR DIABETIC RETINOPATHY DETECTION AND GRADING,” Meas. J. Int. Meas. Confed., vol. 200, no. July, p. 111485, 2022, doi: 10.1016/j.measurement.2022.111485. https://doi.org/10.1016/j.measurement.2022.111485

M. Chiera et al., “HEART RATE VARIABILITY IN THE PERINATAL PERIOD: A CRITICAL AND CONCEPTUAL REVIEW,” Front. Neurosci., vol. 14, no. September, pp. 1–23, 2020, https://doi.org/10.3389/fnins.2020.561186.

Y. O. S. Al-Maraghy, A. S. Sileem, and M. A. E.-A. El-Hameed, “UMBILICAL ARTERY DOPPLER VERSUS NON-STRESS TEST IN HIGH RISK PREGNANCIES,” Al-Azhar Med. J., vol. 49, no. 3, pp. 987–998, 2020, https://doi.org/10.21608/amj.2020.91623

Y. Hirono et al., “THE APPROACH TO SENSING THE TRUE FETAL HEART RATE FOR CTG MONITORING: AN EVALUATION OF EFFECTIVENESS OF DEEP LEARNING WITH DOPPLER ULTRASOUND SIGNALS,” Bioengineering, vol. 11, no. 7, p. 658, 2024, https://doi.org/10.3390/bioengineering11070658.

R. R. Dixit, “PREDICTING FETAL HEALTH USING CARDIOTOCOGRAMS: A MACHINE LEARNING APPROACH,” J. Adv. Anal. Healthc. Manag., vol. 6, no. 1, pp. 43–57, 2022, [Online]. Available: https://research.tensorgate.org/index.php/JAAHM/article/view/38

S. E. Prasetyo, P. H. Prastyo, and S. Arti, “A CARDIOTOCOGRAPHIC CLASSIFICATION USING FEATURE SELECTION: A COMPARATIVE STUDY,” JITCE (Journal Inf. Technol. Comput. Eng., vol. 5, no. 01, pp. 25–32, 2021, https://doi.org/10.25077/jitce.5.01.25-32.2021

M. T. Alam et al., “COMPARATIVE ANALYSIS OF DIFFERENT EFFICIENT MACHINE LEARNING METHODS FOR FETAL HEALTH CLASSIFICATION,” Appl. Bionics Biomech., vol. 2022, 2022, https://doi.org/10.1155/2022/6321884.

A. Callens, D. Morichon, S. Abadie, M. Delpey, and B. Liquet, “USING RANDOM FOREST AND GRADIENT BOOSTING TREES TO IMPROVE WAVE FORECAST AT A SPECIFIC LOCATION,” Appl. Ocean Res., vol. 104, no. July, pp. 1–9, 2020, https://doi.org/10.1016/j.apor.2020.102339.

K. Q. Li and H. L. He, “TOWARDS AN IMPROVED PREDICTION OF SOIL-FREEZING CHARACTERISTIC CURVE BASED ON EXTREME GRADIENT BOOSTING MODEL,” Geosci. Front., vol. 15, no. 6, pp. 1–15, 2024, https://doi.org/10.1016/j.gsf.2024.101898

A. Maulana et al., “MACHINE LEARNING APPROACH FOR DIABETES DETECTION USING FINE-TUNED XGBoost ALGORITHM,” Infolitika J. Data Sci., vol. 1, no. 1, pp. 1–7, 2023, https://doi.org/10.60084/ijds.v1i1.72.

W. Hong et al., “A COMPARISON OF XGBOOST , RANDOM FOREST , AND NOMOGRAPH FOR THE PREDICTION OF DISEASE SEVERITY IN PATIENTS WITH COVID-19 PNEUMONIA : IMPLICATIONS OF CYTOKINE AND IMMUNE CELL PRO FI LE,” Front. Cell. Infect. Microbiol., vol. 12, no. April, pp. 1–13, 2022, https://doi.org/10.3389/fcimb.2022.819267

C. O. Nwokoro, O. A. Duke, and K. C. Nwokoro, “PREDICTING MATERNAL OUTCOMES USING TREE-BASED METHODS IN MACHINE LEARNING,” Res. Sq., pp. 0–14, 2024, https://doi.org/10.21203/rs.3.rs-4359080/v1

D. Chicco and G. Jurman, “THE MATTHEWS CORRELATION COEFFICIENT (MCC) SHOULD REPLACE THE ROC AUC AS THE STANDARD METRIC FOR ASSESSING BINARY CLASSIFICATION,” BioData Min., vol. 16, no. 1, pp. 1–23, 2023, https://doi.org/10.1186/s13040-023-00322-4

K. Najman and K. Zielinkski, “OUTLIER DETECTION WITH THE USE OF ISOLATION FORESTS,” Springer, vol. 1, no. January, pp. 79–65, 2021, https://doi.org/10.1007/978-3-030-75190-6_5

A. E. M. Shokry, M. A. Rizka, and N. M. Labib, “COUNTER TERRORISM FINANCE BY DETECTING MONEY LAUNDERING HIDDEN NETWORKS USING UNSUPERVISED MACHINE LEARNING ALGORITHM,” Int. Conf. ICT, Soc. Hum. Beings 2020, pp. 89–97, 2020, https://doi.org/10.33965/ict_csc_wbc_2020_202008l012.

Z. Cheng, C. Zou, and J. Dong, “OUTLIER DETECTION USING ISOLATION FOREST AND LOCAL OUTLIER FACTOR,” Proc. 2019 Res. Adapt. Converg. Syst. RACS 2019, vol. 19, no. September, pp. 161–168, 2019, https://doi.org/10.1145/3338840.3355641

R. Ripan et al., “AN ISOLATION FOREST LEARNING BASED OUTLIER DETECTION APPROACH FOR EFFECTIVELY CLASSIFYING CYBER ANOMALIES RONY,” Springer Nat. Switz., no. His, pp. 270–279, 2021, doi: https://doi.org/10.1007/978-3-030-73050-5_27.

Larxel, “FETAL HEALTH CLASSIFICATION,” Kaggle, 2020. https://www.kaggle.com/datasets/andrewmvd/fetal-health-classification (accessed Oct. 10, 2024).

V. Kodkin, “CARDIOTOCOGRAPHY IN OBSTETRICS: NEW SOLUTIONS FOR ‘ROUTINE’ TECHNOLOGY,” Sensors, vol. 22, no. 14, 2022, https://doi.org/10.3390/s22145126.

S. Xodo and A. P. Londero, “IS IT TIME TO REDEFINE FETAL DECELERATIONS IN CARDIOTOCOGRAPHY?,” J. Pers. Med., vol. 12, no. 10, 2022, https://doi.org/10.3390/jpm12101552.

N. Rahmayanti, H. Pradani, M. Pahlawan, and R. Vinarti, “COMPARISON OF MACHINE LEARNING ALGORITHMS TO CLASSIFY FETAL HEALTH USING CARDIOTOCOGRAM DATA,” Procedia Comput. Sci., vol. 197, no. 2021, pp. 162–171, 2021, https://doi.org/10.1016/j.procs.2021.12.130.

K. Barnova, R. Martinek, R. Vilimkova Kahankova, R. Jaros, V. Snasel, and S. Mirjalili, ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN ELECTRONIC FETAL MONITORING, vol. 31, no. 5. Springer Netherlands, 2024. https://doi.org/ 10.1007/s11831-023-10055-6.

I. Ben M’Barek, G. Jauvion, and P. F. Ceccaldi, “COMPUTERIZED CARDIOTOCOGRAPHY ANALYSIS DURING LABOR – A STATE-OF-THE-ART REVIEW,” Acta Obstet. Gynecol. Scand., vol. 102, no. 2, pp. 130–137, 2023, https://doi.org/ 10.1111/aogs.14498.

D. Ayres-de-campos, J. Bernardes, A. Garrido, J. Marques-de-sa, and L. Pereira-Leite, “SisPorto 2.0: A PROGRAM FOR AUTOMATED ANALYSIS OF CARDIOTOCOGRAMS,” J. Matern. Fetal. Med., vol. 318, no. November 1999, pp. 311–318, 2000.

D. A. Miller, “ELECTRONIC FETAL HEART RATE MONITORING,” Protoc. High-Risk Pregnancies an Evidence-Based Approach Seventh Ed., pp. 539–553, 2020, https://doi.org/10.1002/9781119635307.ch52.

E. Chandraharan, “TRUE VS SPURIOUS INTRAPARTUM FETAL HEART RATE ACCELERATIONS ON THE CARDIOTOCOGRAPH (CTG): AN URGENT NEED FOR CAUTION,” Glob. J. Reprod. Med., vol. 7, no. 5, 2020, https://doi.org/ 10.19080/gjorm.2019.07.555722.

A. Makins et al., “THE IMPACT OF CASCADE TRAINING — A FIGO AND WHO DEPARTMENT OF SEXUAL AND REPRODUCTIVE HEALTH AND RESEARCH COLLABORATION TO IMPROVE ACCESS TO QUALITY FAMILY PLANNING GLOBALLY,” Int. J. Gynecol. Obstet., vol. 164, no. October 2023, pp. 75–85, 2024, https://doi.org/10.1002/ijgo.15230.

Z. Cheng, C. Zou, and J. Dong, “OUTLIER DETECTION USING ISOLATION FOREST AND LOCAL OUTLIER FACTOR,” Proc. 2019 Res. Adapt. Converg. Syst. RACS 2019, pp. 161–168, 2019, https://doi.org/10.1145/3338840.3355641.

K. Sadaf and J. Sultana, “INTRUSION DETECTION BASED ON AUTOENCODER AND ISOLATION FOREST IN FOG COMPUTING,” IEEE Access, vol. 8, pp. 167059–167068, 2020, https://doi.org/10.1109/ACCESS.2020.3022855.

H. Şevgin, “A COMPARATIVE STUDY OF ENSEMBLE METHODS IN THE FIELD OF EDUCATION: BAGGING AND BOOSTING ALGORITHMS,” Int. J. Assess. Tools Educ., vol. 10, no. 3, pp. 544–562, 2023, https://doi.org/10.21449/ijate.1167705.

J. Tanha, Y. Abdi, N. Samadi, N. Razzaghi, and M. Asadpour, “BOOSTING METHODS FOR MULTI-CLASS IMBALANCED DATA CLASSIFICATION: AN EXPERIMENTAL REVIEW,” J. Big Data, vol. 7, no. 1, 2020, https://doi.org/ 10.1186/s40537-020-00349-y.

V. Angkasa and J. J. Pangaribuan, “INFORMATION SYSTEM DEVELOPMENT KOMPARASI TINGKAT AKURASI RANDOM FOREST DAN KNN UNTUK MENDIAGNOSIS PENYAKIT KANKER PAYUDARA,” J. Inf. Syst. Dev., vol. 7, no. 1, pp. 37–38, 2022, [Online]. Available: http://dx.doi.org/10.19166/xxxx

B. Peng et al., “HarpGBDT: OPTIMIZING GRADIENT BOOSTING DECISION TREE FOR PARALLEL EFFICIENCY,” Proc. - IEEE Int. Conf. Clust. Comput. ICCC, vol. 2019-Septe, pp. 1–11, 2019, https://doi.org/10.1109/CLUSTER.2019.8890990.

E. K. Sahin, “ASSESSING THE PREDICTIVE CAPABILITY OF ENSEMBLE TREE METHODS FOR LANDSLIDE SUSCEPTIBILITY MAPPING USING XGBOOST, GRADIENT BOOSTING MACHINE, AND RANDOM FOREST,” SN Appl. Sci., vol. 2, no. 7, pp. 1–17, 2020, https://doi.org/10.1007/s42452-020-3060-1.

T. Chen and C. Guestrin, “XGBoost: A SCALABLE TREE BOOSTING SYSTEM,” Proc. ACM SIGKDD Int. Conf. Knowl. Discov. Data Min., vol. 13-17-Augu, pp. 785–794, 2016, https://doi.org/10.1145/2939672.2939785.

M. W. Dwinanda, N. Satyahadewi, and W. Andani, “CLASSIFICATION OF STUDENT GRADUATION STATUS USING XGBOOST ALGORITHM,” BAREKENG J. Ilmu Mat. dan Terap., vol. 17, no. 3, pp. 1785–1794, 2023, https://doi.org/10.30598/barekengvol17iss3pp1785-1794.

S. Shrestha, S. Shakya, and A. Jaiswal, “ANALYZING CLOUD TRAFFIC FOR WEB HONEYPOT USING MICROSERVICE-BASED ARCHITECTURE AND XGBoost ALGORITHM,” Proc. 14th IOE Grad. Conf., vol. 14, no. December, pp. 1127–1135, 2023.

P. Blanchard, D. J. Higham, and N. J. Higham, “ACCURATELY COMPUTING THE LOG-SUM-EXP AND SOFTMAX FUNCTIONS,” IMA J. Numer. Anal., vol. 41, no. 4, pp. 2311–2330, 2021, https://doi.org/10.1093/imanum/draa038.

C. M. Ranera, “MULTI-LABEL CLASSIFICATION OF A HYDRAULIC SYSTEM USING MACHINE LEARNING METHODS .,” Programme in Computer, Communication, and Information Sciences, 2022.

H. Pratiwi and E. Zukhronah, “EFFICIENCY AND ACCURACY OF CONVOLUTIONAL AND FOURIER TRANSFORM LAYERS IN NEURAL NETWORKS FOR MEDICAL IMAGE CLASSIFICATION,” BAREKENG J. Ilmu Mat. dan Terap., vol. 18, no. 4, pp. 2387–2396, 2024, https://doi.org/10.30598/barekengvol18iss4pp2387-2396.

D. C. R. Novitasari, A. N. Ramadanti, and D. Z. Haq, “ENHANCING COVID-19 DIAGNOSIS : GLRLM TEXTURE ANALYSIS AND KELM FOR LUNG X-RAY CLASSIFICATION,” Fountain Informatics J., vol. 9, no. 1, pp. 1–8, 2024.

D. C. R. Novitasari, D. Wahyuni, M. Munir, I. Hidayati, F. M. Amin, and K. Oktafianto, “AUTOMATIC DETECTION OF BREAST CANCER IN MAMMOGRAPHIC IMAGE USING THE HISTOGRAM ORIENTED GRADIENT (HOG) DESCRIPTOR AND DEEP RULE BASED (DRB) CLASSIFIER METHOD,” 2019 Int. Conf. Adv. Mechatronics, Intell. Manuf. Ind. Autom. ICAMIMIA 2019 - Proceeding, pp. 185–190, 2019, https://doi.org/10.1109/ICAMIMIA47173.2019.9223361.

H. Al Azies, “INTEGRATING WATER INDICATORS IN A DATA-DRIVEN ARTIFICIAL INTELLIGENCE MODEL FOR FOOD SECURITY CLASSIFICATION,” TheJournalish Soc. Gov., vol. 4, no. 5, pp. 168–180, 2023, https://doi.org/10.55314/tsg.v4i5.607 Hal.

J. M. A. S. Dachi and P. Sitompul, “ANALISIS PERBANDINGAN ALGORITMA XGBOOST DAN ALGORITMA RANDOM FOREST ENSEMBLE LEARNING PADA KLASIFIKASI KEPUTUSAN KREDIT,” J. Ris. Rumpun Mat. Dan Ilmu Pengetah. Alam, vol. 2, no. 2, pp. 87–103, 2023, https://doi.org/10.55606/jurrimipa.v2i2.1470.

N. A. Pramudhyta and M. S. Rohman, “PERBANDINGAN OPTIMASI METODE GRID SEARCH DAN RANDOM SEARCH DALAM ALGORITMA XGBOOST UNTUK KLASIFIKASI STUNTING,” J. Media Inform. Budidarma, vol. 8, no. 1, p. 19, 2024, https://doi.org/10.30865/mib.v8i1.6965.

I. J. Jebadurai, G. J. L. Paulraj, J. Jebadurai, and S. Silas, “EXPERIMENTAL ANALYSIS OF FILTERING-BASED FEATURE SELECTION TECHNIQUES FOR FETAL HEALTH CLASSIFICATION,” Serbian J. Electr. Eng., vol. 19, no. 2, pp. 207–224, 2022, https://doi.org/10.2298/SJEE2202207J.

M. Fasihi, M. H. Nadimi-Shahraki, and A. Jannesari, “A SHALLOW 1-D CONVOLUTION NEURAL NETWORK FOR FETAL STATE ASSESSMENT BASED ON CARDIOTOCOGRAM,” SN Comput. Sci., vol. 2, no. 4, pp. 1–9, 2021, https://doi.org/10.1007/s42979-021-00694-6.

M. Al Duhayyim, S. Abbas, A. Al Hejaili, N. Kryvinska, A. Almadhor, and H. Mughal, “ENSEMBLE LEARNING FOR FETAL HEALTH CLASSIFICATION,” Comput. Syst. Sci. Eng., vol. 47, no. 1, pp. 823–842, 2023, https://doi.org/10.32604/csse.2023.037488.

S. S. Rakhe and A. S. Vaidya, “A SURVEY ON DIFFERENT UNSUPERVISED TECHNIQUES TO DETECT OUTLIERS,” Int. J. Eng. Technol., no. January, pp. 514–519, 2015.

S. Anam, M. R. A. Putra, Z. Fitriah, I. Yanti, N. Hidayat, and D. M. Mahanani, “HEALTH CLAIM INSURANCE PREDICTION USING SUPPORT VECTOR MACHINE WITH PARTICLE SWARM OPTIMIZATION,” BAREKENG J. Ilmu Mat. dan Terap., vol. 17, no. 2, pp. 0797–0806, 2023, https://doi.org/10.30598/barekengvol17iss2pp0797-0806.

Published
2025-07-01
How to Cite
[1]
F. Y. Sari, D. C. Rini Novitasari, A. Hamid, and D. Z. Haq, “FETAL HEALTH RISK STATUS IDENTIFICATION SYSTEM BASED ON CARDIOTOCOGRAPHY DATA USING EXTREME GRADIENT BOOSTING WITH ISOLATION FOREST AS OUTLIER DETECTION”, BAREKENG: J. Math. & App., vol. 19, no. 3, pp. 1711-1724, Jul. 2025.
Issue
Vol 19 No 3 (2025): BAREKENG: Journal of Mathematics and Its Application
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Articles

Copyright (c) 2025 Firda Yunita Sari, Dian Candra Rini Novitasari, Abdulloh Hamid, Dina Zatusiva Haq

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