Ensemble Techniques Based Risk Classification for Maternal Health During Pregnancy
Nurul Fathanah Mustamin(1); Ariyani Buang(2); Firman Aziz(3*); Nur Hamdani Nur(4);
(1) Universitas Lambung Mangkurati
(2) Universitas Pancasakti
(3) Universitas Pancasakti
(4) Universitas Pancasakti
(*) Corresponding Author
AbstractThis research focuses on the critical aspect of maternal health during pregnancy, emphasizing the need for early detection and intervention to address potential risks to both mothers and infants. Leveraging various classification methods, including Naïve Bayes, decision trees, and ensemble learning techniques, the study investigates the prediction of childbirth potential and pregnancy risks. The research begins with data collection, followed by preprocessing to clean and prepare the data, including handling missing values and normalization. Next, cross-validation is performed to ensure model robustness. Five ensemble techniques are used for risk classification: Ensemble Boosted Trees, which enhances the performance of decision trees; Ensemble Bagged Trees, which combines predictions from decision trees trained on different subsets of data; Ensemble Subspace Discriminant, which applies discriminant analysis on random subspaces; Ensemble Subspace KNN, which uses K-Nearest Neighbors (KNN) within random subspaces; and Ensemble RUS Boosted Trees. Key variables such as maternal age, height, Hb levels, blood pressure, and previous pregnancy history are considered in these analyses. Additionally, the study introduces Ensemble Learning based on Classification Trees, revealing significant improvements in accuracy compared to cost-sensitive learning approaches. The comparison of methods, including Naïve Bayes and K-Nearest Neighbor, provides insights into their respective performances, with ensemble techniques demonstrating their potential. The proposed ensemble learning techniques, namely Ensemble Boosted Trees, Ensemble Bagging Trees, Ensemble Subspace Discriminant, Ensemble Subspace KNN, and Ensemble RUS Boosted Trees, are systematically evaluated in classifying pregnancy risks based on a comprehensive dataset of 1014 records. The results showcase Ensemble Bagging Trees as a standout performer, with an accuracy of 85.6%, indicating robust generalization and effectiveness in clinical risk assessment compared to traditional methods such as Decision Tree (61.54% accuracy), K-Nearest Neighbor (74.48%), Ensemble Learning based on Cost-Sensitive Learning (73%), Ensemble Learning based on Classification Tree (76%), Gaussian Naïve Bayes (82.6%), Multinomial Naïve Bayes (84.8%), and Bernoulli Naïve Bayes (84.8%). Ensemble Bagging Trees achieved the highest accuracy proving to be more effective than the other methods. However, the study emphasizes the need for continuous refinement and adaptation of ensemble methods, considering both accuracy and interpretability, for successful deployment in healthcare decision-making. These findings contribute valuable insights into optimizing pregnancy risk classification models, paving the way for improved maternal and infant healthcare outcomes.
KeywordsClassification; Ensemble; Health Risk; Machine Learning; Maternal
|
Full Text:PDF |
Article MetricsAbstract view: 6 timesPDF view: 2 times |
Digital Object Identifier![]() |
Cite |
References
I. Margret, K. Rajakumar, … K. A.-I., and undefined 2024, “Machine Learning-Based Box Models for Pregnancy Care and Maternal Mortality Reduction: A Literature Survey,” ieeexplore.ieee.org, Accessed: Jun. 25, 2024.
C. Oyston, C. Rueda-Clausen, P. B.- Obstetrics, & G., and undefined 2017, “Current challenges in pregnancy-related mortality,” Elsevier, vol. 5, no. 48, p. 12, 2017, doi: 10.22038/ijp.2017.26983.2325.
Z. S. Lassi et al., “Systematic review on human resources for health interventions to improve maternal health outcomes: evidence from low-and middle-income countries,” Springer, vol. 14, no. 1, Mar. 2016, doi: 10.1186/s12960-016-0106-y.
S. G. Ahmad et al., “Sensing and artificial intelligent maternal-infant health care systems: a review,” mdpi.com, 2022, doi: 10.3390/s22124362.
S. Rani and M. Kumar, “Prediction of the mortality rate and framework for remote monitoring of pregnant women based on IoT,” Multimed. Tools Appl., vol. 80, no. 16, pp. 24555–24571, Jul. 2021, doi: 10.1007/S11042-021-10823-1.
M. Islam, T. Mahmud, N. Khan, … S. M.-I., and undefined 2020, “Exploring machine learning algorithms to find the best features for predicting modes of childbirth,” ieeexplore.ieee.org, Accessed: Jun. 25, 2024.
B. Lakshmi, … T. I.-, undefined Communications, undefined and, and undefined 2015, “A comparative study of classification algorithms for predicting gestational risks in pregnant women,” ieeexplore.ieee.org, Accessed: Jun. 25, 2024.
M. Ahmed, M. K.-2020 2nd I. C. on, and undefined 2020, “IoT based risk level prediction model for maternal health care in the context of Bangladesh,” ieeexplore.ieee.org, doi: 10.1109/STI50764.2020.9350320.
A. Subasi, B. Kadasa, E. K.-P. C. Science, and undefined 2020, “Classification of the cardiotocogram data for anticipation of fetal risks using bagging ensemble classifier,” Elsevier, Accessed: Jun. 25, 2024.
N. Puspitasari, … A. B.-… J. of O. &, and undefined 2022, “Naïve Bayes and K-Nearest Neighbor Algorithms Performance Comparison in Diabetes Mellitus Early Diagnosis.,” search.ebscohost.com, Accessed: Jun. 25, 2024.
S. Venkatesh, H. Jha, F. Kazmi, and S. Zaidi, “Classification of Maternal Health Risks Using Machine Learning Methods,” ehbconference.ro, Accessed: Dec. 15, 2023.
L. Pawar, J. Malhotra, … A. S.-2022 3rd I., and undefined 2022, “A Robust Machine Learning Predictive Model for Maternal Health Risk,” ieeexplore.ieee.org, Accessed: Dec. 13, 2023.
W. T. Wu et al., “Data mining in clinical big data: the frequently used databases, steps, and methodological models,” Mil. Med. Res., vol. 8, no. 1, Dec. 2021, doi: 10.1186/S40779-021-00338-Z.
B. Falkner, “Maternal and gestational influences on childhood blood pressure,” Pediatr. Nephrol., vol. 35, no. 8, pp. 1409–1418, Aug. 2020, doi: 10.1007/S00467-019-4201-X.
X. Dong, Z. Yu, W. Cao, Y. Shi, Q. M.-F. of C. Science, and undefined 2020, “A survey on ensemble learning,” Springer, vol. 2020, no. 2, pp. 241–258, Apr. 2020, doi: 10.1007/s11704-019-8208-z.
N. Hardiyanti, A. Lawi, Diaraya, and F. Aziz, “Classification of Human Activity based on Sensor Accelerometer and Gyroscope Using Ensemble SVM method,” Proc. - 2nd East Indones. Conf. Comput. Inf. Technol. Internet Things Ind. EIConCIT 2018, pp. 304–307, Nov. 2018, doi: 10.1109/EICONCIT.2018.8878627.
Z. Said, P. Sharma, A. Tiwari, Z. Huang, … V. B.-J. of C., and undefined 2022, “Application of novel framework based on ensemble boosted regression trees and Gaussian process regression in modelling thermal performance of small-scale,” Elsevier, Accessed: May 15, 2024.
A. Lawi, F. Aziz, and S. Syarif, “Ensemble GradientBoost for increasing classification accuracy of credit scoring,” Proc. 2017 4th Int. Conf. Comput. Appl. Inf. Process. Technol. CAIPT 2017, vol. 2018-January, pp. 1–4, Mar. 2018, doi: 10.1109/CAIPT.2017.8320700.
P. Yariyan et al., “Improvement of Best First Decision Trees Using Bagging and Dagging Ensembles for Flood Probability Mapping,” Water Resour. Manag., vol. 34, no. 9, pp. 3037–3053, Jul. 2020, doi: 10.1007/S11269-020-02603-7.
F. Aziz, A. Lawi, and E. Budiman, “Increasing Accuracy of Ensemble Logistics Regression Classifier by Estimating the Newton Raphson Parameter in Credit Scoring,” ieeexplore.ieee.org, 2019, doi: 10.1109/CAIPT.2017.8320700.
S. Patil and A. K. Jalan, “Ensemble Subspace Discriminant Classifiers for Misalignment Fault Classification Using Vibro-acoustic Sensor Data Fusion,” J. Vib. Eng. Technol., vol. 10, no. 8, pp. 3169–3178, Nov. 2022, doi: 10.1007/S42417-022-00548-2.
Y. Zhang, G. Cao, B. Wang, X. L.-P. Recognition, and undefined 2019, “A novel ensemble method for k-nearest neighbor,” Elsevier, Accessed: May 15, 2024.
N. Noor, H. Ibrahim, M. Lah, J. A.-I. Access, and undefined 2021, “Improving outcome prediction for traumatic brain injury from imbalanced datasets using RUSBoosted trees on electroencephalography spectral power,” ieeexplore.ieee.org, Accessed: Jun. 25, 2024.
A. Oprescu, G. Miro-Amarante, … L. G.-D.-I., and undefined 2020, “Artificial intelligence in pregnancy: A scoping review,” ieeexplore.ieee.org, Accessed: Jun. 25, 2024.
E. Tunstel, M. Cobo, … E. H.-V.-I. T., and undefined 2020, “Systems science and engineering research in the context of systems, man, and cybernetics: Recollection, trends, and future directions,” ieeexplore.ieee.org, Accessed: Jun. 25, 2024.
S. García, S. Ramírez-Gallego, J. Luengo, J. M. Benítez, and F. Herrera, “Big data preprocessing: methods and prospects,” Big Data Anal., vol. 1, no. 1, Dec. 2016, doi: 10.1186/S41044-016-0014-0.
F. Kamiran, T. C.-K. and information systems, and undefined 2012, “Data preprocessing techniques for classification without discrimination,” Springer, vol. 33, no. 1, pp. 1–33, 2011, doi: 10.1007/s10115-011-0463-8.
S. Saud, B. Jamil, Y. Upadhyay, K. I.-S. E. Technologies, and undefined 2020, “Performance improvement of empirical models for estimation of global solar radiation in India: A k-fold cross-validation approach,” Elsevier, vol. 40, 2020, doi: 10.1016/j.seta.2020.100768.
T. Khoshgoftaar, J. Van Hulse, C. Seiffert, T. M. Khoshgoftaar, and A. Napolitano, “RUSBoost: A hybrid approach to alleviating class imbalance,” ieeexplore.ieee.orgC Seiffert, TM Khoshgoftaar, J Van Hulse, A NapolitanoIEEE Trans. Syst. man, Cybern. A Syst. 2009•ieeexplore.ieee.org, vol. 40, no. 1, 2010, doi: 10.1109/TSMCA.2009.2029559.
A. Salih, A. A.-J. of S. C. and, and undefined 2021, “Evaluation of classification algorithms for intrusion detection system: A review,” publisher.uthm.edu.my, vol. 2, no. 1, pp. 31–40, 2021, doi: 10.30880/jscdm.2021.02.01.004.
Refbacks
- There are currently no refbacks.
Copyright (c) 2024 Nurul Fathanah Mustamin, Ariyani Buang, Firman Aziz, Nur Hamdani Nur
![Creative Commons License](http://licensebuttons.net/l/by-sa/4.0/88x31.png)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.