Enhancing Maternal Outcome Prediction Using Explainable Artificial Intelligence for Women of Childbearing Age

Authors

  • Chukwudi Obinna Nwokoro Department of Computer Science, Faculty of Science, University of Uyo, Nigeria & TEFTFund Centre of Excellence in Computational Intelligence Research, University of Uyo, Nigeria
  • Udoinyang G. Inyang Department of Computer Science, Faculty of Science, University of Uyo, Nigeria & TEFTFund Centre of Excellence in Computational Intelligence Research, University of Uyo, Nigeria
  • Imo J. Eyoh Department of Computer Science, Faculty of Science, University of Uyo, Nigeria & TEFTFund Centre of Excellence in Computational Intelligence Research, University of Uyo, Nigeria
  • U. A. Umoh Department of Computer Science, Faculty of Science, University of Uyo, Nigeria & TEFTFund Centre of Excellence in Computational Intelligence Research, University of Uyo, Nigeria
  • Onyeabochukwu Augustine Duke Department of Obstetrician and Gynaecologist, University of Nigeria Teaching hospital, Enugu State, Nigeria
  • Kelechi C. Nwokoro Department of Paediatrics, University of Port-Harcourt Teaching Hospital, River State, Nigeria
  • Peace I. Opara Department of Paediatrics, University of Port-Harcourt Teaching Hospital, River State, Nigeria
  • Chinmanma Obinachi Department of Education, Ignatius Ajuru University of Education, River State, Nigeria
  • Joseph U. Kingsley TEFTFund Centre of Excellence in Computational Intelligence Research, University of Uyo, Nigeria

DOI:

https://doi.org/10.51983/ajsat-2023.12.2.4072

Keywords:

Shapley Additive Attribution, ML, Interpretability, Feature Relevance, Maternal Outcome

Abstract

Our research has identified a significant shift in the utilization of machine learning models for prediction and decision-making. Rather than solely relying on these models, there is now a growing focus on their explainability and interpretability. Data for this study were acquired between 2019 and 2022 in the Nigerian Coastal Plain, encompassing information from two thousand patients collected at secondary and tertiary healthcare centers, comprising fifteen distinct features. Our team utilized the Shapley additive explanation (SHAP) method to achieve this goal, adhering to the principle of fair play from game theory, giving every maternal attribute used in our work equal consideration. With an RF model and explainable AI techniques, we aimed to predict maternal outcomes and provide comprehensive insights into the most important features of mothers within the bearing age group. We summarized the study’s findings in terms of demography, laboratory results, physical examination, and mode of delivery. Our analysis revealed that mothers of an older age are more likely to experience a caesarean section or have a child with Down syndrome. However, we also found that the SHAP method, along with other XAI methods such as LIME and CIU, can play a vital role in improving satisfaction, time, and understanding. This approach can greatly improve medical decision-making, benefiting both mothers and their children. Our confidence in these findings is high, and we believe they will have a noteworthy impact on the field of parental health.

References

P. Behzadi, E. Behzadi, and E. A. Pawlak-Adamska, “Urinary tractinfections (UTIs) or genital tract infections (GTIs)? It’s thediagnostics that count,” GMS Hyg. Infect. Control, vol. 14, pp. 1-5, 2019. doi: 10.3205/dgkh000320.

B. Clevenger and T. Richards, “Pre-operative anaemia,” Anaesthesia, vol. 70, no. s1, pp. 20-e8, 2015. doi: 10.1111/anae.12918.

E. Duncan, C. Best, and S. Hagen, “Shared decision makinginterventions for people with mental health conditions,” Cochrane Database Syst. Rev, no. 1, 2010. doi: 10.1002/14651858.CD007297.pub2.

K. T. Mills, A. Stefanescu, and J. He, “The global epidemiology ofhypertension,” Nat. Rev. Nephrol., vol. 16, no. 4, Apr. 2020. doi: 10.1038/s41581-019-0244-2.

R. L. Goldenberg et al., “Diabetes during Pregnancy in Low- andMiddle-Income Countries,” Am. J. Perinatol., vol. 33, no. 13,pp. 1227-1235, Nov. 2016. doi: 10.1055/s-0036-1584152.

J. Hone and L. Jovanovič, “Approach to the Patient with Diabetesduring Pregnancy,” J. Clin. Endocrinol. Metab., vol. 95, no. 8,pp. 3578-3585, Aug. 2010. doi: 10.1210/jc.2010-0383.

S. Schlesinger et al., “Food Groups and Risk of Overweight, Obesity,and Weight Gain: A Systematic Review and Dose-Response Meta-

Analysis of Prospective Studies,” Adv. Nutr., vol. 10, no. 2, pp. 205-218, Mar. 2019. doi: 10.1093/advances/nmy092.

L. Dodds et al., “Outcomes of Pregnancies Complicated byHyperemesis Gravidarum,” Obstet. Gynecol., vol. 107, no. 2 Part 1,pp. 285-292, Feb. 2006. doi: 10.1097/01.AOG.0000195060.22832.cd.

A. L. Drake et al., “Incident HIV during Pregnancy and Postpartumand Risk of Mother-to-Child HIV Transmission: A SystematicReview and Meta-Analysis,” PLOS Med., vol. 11, no. 2,pp. e1001608, Feb. 2014. doi: 10.1371/journal.pmed.1001608.

U. Irshad, H. Mahdy, and T. Tonismae, HIV in Pregnancy. StatPearlsPublishing, 2022. [Online]. Available: https://www.ncbi.nlm.nih.gov/books/NBK558972/

N. A. Terrault et al., “Viral hepatitis and pregnancy,” Nat. Rev.Gastroenterol. Hepatol., vol. 18, no. 2, Feb. 2021. doi: 10.1038/s41575-020-00361-w.

S. P. B. Jaiswal et al., “Viral hepatitis during pregnancy,” Int. J.Gynecol. Obstet., vol. 72, no. 2, pp. 103-108, Feb. 2001. doi: 10.1016S0020-7292(00)00264-2.

P. Ormerod, “Tuberculosis in pregnancy and the puerperium,”Thorax, vol. 56, no. 6, pp. 494-499, Jun. 2001. doi: 10.1136/thx.56.6.494.

O. M. Loto and I. Awowole, “Tuberculosis in Pregnancy: A Review,”J.Pregnancy, vol. 2012, p. e379271, Nov. 2011. doi: 10.1155/2012/379271.

United Nations Internationals Children Emergency, “UNICEF,”2023. https://www.unicef.org/ (accessed Jan. 19, 2023).

UNICEF, “Maternal mortality rates and statistics,” UNICEF DATA,2021. https://data.unicef.org/ topic/ maternal-health/maternal-mortality/ (accessed Nov. 15, 2022).

C. Huang et al., “Using Deep Learning in a Monocentric Study toCharacterize Maternal Immune Environment for PredictingPregnancy Outcomes in the Recurrent Reproductive Failure Patients,”Front. Immunol., vol. 12, 2021. [Online]. Available: https://www.frontiersin.org/articles/10.3389/fimmu.2021. 642167

J. H. Jhee et al., “Prediction model development of late-onset preeclampsia using machine learning-based methods,” PLOS ONE,vol. 14, no. 8, pp. e0221202, Aug. 2019. doi: 10.1371/journal.pone.0221202.

U. G. Inyang et al., “Predictive Decision Support Analytic Model forIntelligent Obstetric Risks Management,” in International Conference on Emerging Applications and Technologies for Industry 4.0(EATI’2020), 2021, pp. 92-108. doi: 10.1007/978-3-030-80216-5_8.

K. Betts, S. Kisely, and R. Alati, “Predicting common maternalpostpartum complications: leveraging health administrative data andmachine learning,” BJOG Int. J. Obstet. Gynaecol., vol. 126, no. 6,pp. 702-709, 2019. doi: 10.1111/1471-0528.15607.

J. Guedalia et al., “Real-time data analysis using a machine learningmodel significantly improves prediction of successful vaginaldeliveries,” Am. J. Obstet. Gynecol., vol. 223, no. 3, pp. 437.e1-437.e15, Sep. 2020. doi: 10.1016/j.ajog.2020.05.025.

M. Lipschuetz et al., “Prediction of vaginal birth after cesareandeliveries using machine learning,” Am. J. Obstet. Gynecol., vol. 222,no. 6, pp. 613.e1-613.e12, Jun. 2020. doi: 10.1016/j.ajog.2019.12.267.

B. Hammoud, A. Semaan, I. Elhajj, and L. Benova, “Can machinelearning models predict maternal and newborn healthcare providers’perception of safety during the COVID-19 pandemic? A cross-sectional study of a global online survey,” Hum. Resour. Health,vol. 20, no. 1, p. 63, Aug. 2022. doi: 10.1186/s 12960-022-00758-5.

C. Meske and E. Bunde, “Transparency and Trust in Human-AI-Interaction: The Role of Model-Agnostic Explanations in ComputerVision-Based Decision Support,” in Artificial Intelligence in HCI, Cham, 2020, pp. 54-69. doi: 10.1007/978-3-030-50334-5_4.

Y. Nohara, K. Matsumoto, H. Soejima, and N. Nakashima,“Explanation of machine learning models using shapley additiveexplanation and application for real data in hospital,” Comput.Methods Programs Biomed., vol. 214, p. 106584, Feb. 2022.doi: 10.1016/j.cmpb.2021.106584.

E. Albini, J. Long, D. Dervovic, and D. Magazzeni, “CounterfactualShapley Additive Explanations,” in 2022 ACM Conference onFairness, Accountability, and Transparency, New York, NY, USA,Jun. 2022, pp. 1054-1070. doi: 10.1145/3531146.3533168.

T. Freiesleben, “The Intriguing Relation Between CounterfactualExplanations and Adversarial Examples,” Minds Mach., vol. 32,no. 1, pp. 77-109, Mar. 2022. doi: 10.1007/s11023-021-09580-9.

T. M. Bosschieter et al., “Using Interpretable Machine Learning toPredict Maternal and Fetal Outcomes”. arXiv, Jul. 12, 2022. [Online].Available: http://arxiv.org/abs/2207.05322

S. Wesołowski et al., “An explainable artificial intelligence approachfor predicting cardiovascular outcomes using electronic healthrecords,” PLOS Digit. Health, vol. 1, no. 1, p. e0000004, Jan. 2022.doi: 10.1371/journal.pdig.0000004.

S. M. Lauritsen et al., “Explainable artificial intelligence model topredict acute critical illness from electronic health records,” Nat.Commun., vol. 11, no. 1, Art. no. 1, Jul. 2020. doi: 10.1038/s41467-020-17431-x.

G. Marvin and Md. G. R. Alam, “Explainable Feature Learning forPredicting Neonatal Intensive Care Unit (NICU) Admissions,” in2021 IEEE International Conference on Biomedical Engineering,Computer and Information Technology for Health (BECITHCON),Dec. 2021, pp. 69-74. doi: 10.1109/BECITHCON54710.2021.9893719.

S. Knapič, A. Malhi, R. Saluja, and K. Främling, “Explainable Artificial Intelligence for Human Decision Support System in theMedical Domain,” Mach. Learn. Knowl. Extr., vol. 3, no. 3, pp. no. 3, Sep. 2021. doi: 10.3390/make3030037.

U. Pawar, D. O’Shea, S. Rea, and R. O’Reilly, “Explainable AI inHealthcare,” in 2020 International Conference on Cyber SituationalAwareness, Data Analytics and Assessment (CyberSA), Jun. 2020,pp. 1-2. doi: 10.1109/CyberSA49311.2020.9139655.

S. Khedkar, V. Subramanian, G. Shinde, and P. Gandhi, “ExplainableAI in Healthcare”. Rochester, NY, Apr. 08, 2019. doi: 10.2139/ssrn.3367686.

A. Holzinger, C. Biemann, C. S. Pattichis, and D. B. Kell, “What dowe need to build explainable AI systems for the medical domain?”arXiv, Dec. 28, 2017. doi: 10.48550/arXiv.1712.09923.

G. Cinà, T. Röber, R. Goedhart, and I. Birbil, “Why we do needExplainable AI for Healthcare”. arXiv, Jun. 30, 2022. doi: 10.48550/arXiv.2206.15363.

A. Holzinger, “Explainable AI and Multi-Modal Causability inMedicine,” -Com, vol. 19, no. 3, pp. 171-179, Dec. 2020. doi: 10.1515/icom-2020-0024.

Z. Papanastasopoulos et al., “Explainable AI for medical imaging:deep-learning CNN ensemble for classification of estrogen receptorstatus from breast MRI,” in Medical Imaging 2020: Computer-AidedDiagnosis, Mar. 2020, vol. 11314, pp. 228-235. doi: 10.1117/12.2549298.

T. Folke, S. C.-H. Yang, S. Anderson, and P. Shafto, “Explainable AIfor medical imaging: explaining pneumothorax diagnoses withBayesian teaching,” in Artificial Intelligence and Machine Learningfor Multi-Domain Operations Applications III, Apr. 2021, vol. 11746,pp. 644-664.

S. M. Muddamsetty, M. N. S. Jahromi, and T. B. Moeslund, “ExpertLevel Evaluations for Explainable AI (XAI) Methods in the MedicalDomain,” in Pattern Recognition. ICPR International Workshops andChallenges, Cham, 2021, pp. 35-46. doi: 10.1007/978-3-030-68796-0_3.

S. Meacham, G. Isaac, D. Nauck, and B. Virginas, “TowardsExplainable AI: Design and Development for Explanation ofMachine Learning Predictions for a Patient Readmittance MedicalApplication,” in Intelligent Computing, Cham, 2019, pp. 939-955. doi: 10.1007/978-3-030-22871-2_67.

T. A. J. Schoonderwoerd, W. Jorritsma, M. A. Neerincx, and K. vanden Bosch, “Human-centered XAI: Developing design patterns forexplanations of clinical decision support systems,” Int. J. Hum.-Comput. Stud., vol. 154, p. 102684, Oct. 2021. doi: 10.1016/j.ijhcs.2021.102684.

A. Anguita-Ruiz, A. Segura-Delgado, R. Alcalá, C. M. Aguilera, andJ.Alcalá-Fdez, “eXplainable Artificial Intelligence (XAI) for theidentification of biologically relevant gene expression patterns inlongitudinal human studies, insights from obesity research,” PLOSComput. Biol., vol. 16, no. 4, p. e1007792, Apr. 2020. doi: 10.1371/journal.pcbi.1007792.

J. Jiménez-Luna, F. Grisoni, and G. Schneider, “Drug discovery withexplainable artificial intelligence,” Nat. Mach. Intell., vol. 2, no. 10,Art. no. 10, Oct. 2020. doi: 10.1038/s42256-020-00236-4.

M. Ghassemi, L. Oakden-Rayner, and A. L. Beam, “The false hope ofcurrent approaches to explainable artificial intelligence in healthcare,” Lancet Digit. Health, vol. 3, no. 11, pp. e745-e750, Nov. 2021.doi: 10.1016/S2589-7500(21)00208-9.

M. Ahmed and S. Zubair, “Explainable Artificial Intelligence inSustainable Smart Healthcare,” in Explainable Artificial Intelligencefor Cyber Security: Next Generation Artificial Intelligence, M.Ahmed, S. R. Islam, A. Anwar, N. Moustafa, and A.-S. K. Pathan,Eds. Cham: Springer International Publishing, 2022, pp. 265-280. doi: 10.1007/978-3-030-96630-0_12.

O. I. Dauda, J. B. Awotunde, M. AbdulRaheem, and S. A. Salihu,“Basic Issues and Challenges on Explainable Artificial Intelligence(XAI) in Healthcare Systems,” Principles and Methods ofExplainable Artificial Intelligence in Healthcare, 2022. [Online].Available: https://www.igi-global.com/chapter/basic-issues-and-challenges-on-explainable-artificial-intelligence-xai-in-healthcare-systems/www.igi-global.com/chapter/basic-issues-and-challenges-on-explainable-artificial-intelligence-xai-in-healthcare-systems/304184(accessed Nov. 19, 2022).

F. K. Došilović, M. Brčić, and N. Hlupić, “Explainable artificialintelligence: A survey,” in 2018 41st International Convention onInformation and Communication Technology, Electronics andMicroelectronics (MIPRO), May 2018, pp. 0210-0215.doi: 10.23919/MIPRO.2018.8400040.

A. Adadi and M. Berrada, “Peeking Inside the Black-Box: A Surveyon Explainable Artificial Intelligence (XAI),” IEEE Access, vol. 6,pp. 52138-52160, 2018. doi: 10.1109/ACCESS.2018.2870052.

E. Tjoa and C. Guan, “A Survey on Explainable ArtificialIntelligence (XAI): Toward Medical XAI,” IEEE Trans. NeuralNetw. Learn. Syst., vol. 32, no. 11, pp. 4793-4813, Nov. 2021.doi: 10.1109/TNNLS.2020.3027314.

S. R. Islam, W. Eberle, S. K. Ghafoor, and M. Ahmed, “ExplainableArtificial Intelligence Approaches: A Survey,” arXiv, Jan. 23, 2021.doi: 10.48550/arXiv.2101.09429.

A. Das and P. Rad, “Opportunities and Challenges in ExplainableArtificial Intelligence (XAI): A Survey,” arXiv, Jun. 22, 2020.doi: 10.48550/arXiv.2006.11371.

I. Ahmed, G. Jeon, and F. Piccialli, “From Artificial Intelligence toExplainable Artificial Intelligence in Industry 4.0: A Survey on What,How, and Where,” IEEE Trans. Ind. Inform., vol. 18, no. 8, pp. 5031-5042, Aug. 2022. doi: 10.1109/TII.2022.3146552.

T. Rojat, R. Puget, D. Filliat, J. Del Ser, R. Gelin, and N. Díaz-Rodríguez, “Explainable Artificial Intelligence (XAI) on TimeSeriesData: A Survey,” arXiv, Apr. 02, 2021. doi: 10.48550/arXiv.2104.00950.

A. Vassiliades, N. Bassiliades, and T. Patkos, “Argumentation andexplainable artificial intelligence: a survey,” Knowl. Eng. Rev., vol. 36, p. e5, 2021. doi: 10.1017/S0269888921000011.

F. Di Martino and F. Delmastro, “Explainable AI for clinical andremote health applications: a survey on tabular and time series data,”Artif. Intell. Rev., Oct. 2022. doi: 10.1007/s10462-022-10304-3.

U. Inyang, S. A., F. Ijebu, I. J., and C. O., “Optimality Assessmentsof Classifiers on Single and Multi-labelled Obstetrics OutcomeClassification Problems,” Int. J. Adv. Comput. Sci. Appl., vol. 12, Jan.2021. doi: 10.14569/IJACSA.2021.0120260.

F. Wang, Y. Wang, X. Ji, and Z. Wang, “Effective MacrosomiaPrediction Using Random Forest Algorithm,” Int. J. Environ. Res.Public. Health, vol. 19, no. 6, Art. no. 6, Jan. 2022. doi: 10.3390/ijerph19063245.

T. Enebish et al., “The acute lag effects of elevated ambient airpollution on stillbirth risk in Ulaanbaatar, Mongolia,” medRxiv, p.2022.02.17.22271117, Feb. 17, 2022. doi: 10.1101/2022.02.17.22271117.

P. Behzadi, E. Behzadi, and E. A. Pawlak-Adamska, “Urinary tractinfections (UTIs) or genital tract infections (GTIs)? It’s thediagnostics that count,” GMS Hyg. Infect. Control, vol. 14, pp. 1-5, 2019. [Online]. Available: doi: 10.3205/dgkh000320.

B. Clevenger and T. Richards, “Pre-operative anaemia,” Anaesthesia, vol. 70, no. s1, pp. 20-e8, 2015. [Online]. Available: doi: 10.1111/anae.12918.

E. Duncan, C. Best, and S. Hagen, “Shared decision makinginterventions for people with mental health conditions,” Cochrane Database Syst. Rev., no. 1, 2010. [Online]. Available: doi: 10.1002/14651858.CD007297.pub2.

K. T. Mills, A. Stefanescu, and J. He, “The global epidemiology ofhypertension,” Nat. Rev. Nephrol., vol. 16, no. 4, Apr. 2020. [Online]. Available: doi: 10.1038/s41581-019-0244-2.

R. L. Goldenberg et al., “Diabetes during Pregnancy in Low- andMiddle-Income Countries,” Am. J. Perinatol., vol. 33, no. 13, pp.1227-1235, Nov. 2016. [Online]. Available: doi: 10.1055/s-0036-158 4152.

J. Hone and L. Jovanovič, “Approach to the Patient with Diabetesduring Pregnancy,” J. Clin. Endocrinol. Metab., vol. 95, no. 8, pp.3578-3585, Aug. 2010. [Online]. Available: doi: 10.1210/jc.2010-0383.

S. Schlesinger et al., “Food Groups and Risk of Overweight, Obesity,and Weight Gain: A Systematic Review and Dose-Response Meta-Analysis of Prospective Studies,” Adv. Nutr., vol. 10, no. 2, pp. 205-218, Mar. 2019. [Online]. Available: doi: 10.1093/advances/nmy092.

L. Dodds et al., “Outcomes of Pregnancies Complicated byHyperemesis Gravidarum,” Obstet. Gynecol., vol. 107, no. 2 Part 1,pp. 285-292, Feb. 2006. [Online]. Available: doi: 10.1097/01.AOG.0000195060.22 832.cd.

A. L. Drake et al., “Incident HIV during Pregnancy and Postpartumand Risk of Mother-to-Child HIV Transmission: A SystematicReview and Meta-Analysis,” PLOS Med., vol. 11, no. 2, p. e1001608,Feb. 2014. [Online]. Available: doi: 10.1371/journal.pmed.1001608.

U. Irshad, H. Mahdy, and T. Tonismae, “HIV in Pregnancy”.StatPearls Publishing, 2022. [Online]. Available: https://www.ncbi.nlm.nih.gov/books/NBK558972/

N. A. Terrault et al., “Viral hepatitis and pregnancy,” Nat. Rev.Gastroenterol. Hepatol., vol. 18, no. 2, Feb. 2021. [Online].Available: doi: 10.1038/s41575-020-00361-w.

S. P. B. Jaiswal et al., “Viral hepatitis during pregnancy,” Int. J.Gynecol. Obstet., vol. 72, no. 2, pp. 103-108, Feb. 2001. [Online].Available: doi: 10.1016/S0020-7292(00)00264-2.

P. Ormerod, “Tuberculosis in pregnancy and the puerperium,”Thorax, vol. 56, no. 6, pp. 494-499, Jun. 2001. [Online]. Available:doi: 10.1136/thx.56.6.494.

O. M. Loto and I. Awowole, “Tuberculosis in Pregnancy: A Review,”J.Pregnancy, vol. 2012, p. e379271, Nov. 2011. [Online]. Available:doi: 10.1155/2012/379271.

United Nations Internationals Children Emergency, “UNICEF,”2023. [Online]. Available: https://www.unicef.org/ (accessed Jan. 19,2023).

UNICEF, “Maternal mortality rates and statistics,” UNICEF DATA,2021. [Online]. Available: https://data.unicef.org/topic/maternal-health/maternal-mortality/ (accessed Nov. 15, 2022).

C. Huang et al., “Using Deep Learning in a Monocentric Study toCharacterize Maternal Immune Environment for PredictingPregnancy Outcomes in the Recurrent Reproductive Failure Patients,”Front. Immunol., vol. 12, 2021. [Online]. Available:https://www.frontiersin.org/articles/10.3389/fimmu.2021.642167

J. H. Jhee et al., “Prediction model development of late-onset preeclampsia using machine learning-based methods,” PLOS ONE,vol. 14, no. 8, p. e0221202, Aug. 2019. [Online]. Available:doi: 10.1371/journal.pone.0221202.

U. G. Inyang et al., “Predictive Decision Support Analytic Model forIntelligent Obstetric Risks Management,” in International Conference on Emerging Applications and Technologies for Industry 4.0(EATI’2020), 2021, pp. 92-108. [Online]. Available: doi: 10.1007/978-3-030-80216-5_8.

K. Betts, S. Kisely, and R. Alati, “Predicting common maternalpostpartum complications: leveraging health administrative data andmachine learning,” BJOG Int. J. Obstet. Gynaecol., vol. 126, no. 6,pp. 702-709, 2019. [Online]. Available: doi: 10.1111/1471-0528.15607.

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07-12-2023

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Obinna Nwokoro, C., Inyang, U. G., Eyoh, I. J., Umoh, U. A., Augustine Duke, O., Nwokoro, K. C., Opara, P. I., Obinachi, C., & Kingsley, J. U. (2023). Enhancing Maternal Outcome Prediction Using Explainable Artificial Intelligence for Women of Childbearing Age. Asian Journal of Science and Applied Technology, 12(2), 44–55. https://doi.org/10.51983/ajsat-2023.12.2.4072

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Vol. 12 No. 2 (2023): July-December 2023

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