Call for Paper - January 2023 Edition
IJCA solicits original research papers for the January 2023 Edition. Last date of manuscript submission is December 20, 2022. Read More

Enriching Quality of Maternal Health Care through Machine Learning

Print
PDF
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2020
Authors:
Bolanle F. Oladejo, Oladejo Olajide A.
10.5120/ijca2020919821

Bolanle F Oladejo and Oladejo Olajide A.. Enriching Quality of Maternal Health Care through Machine Learning. International Journal of Computer Applications 177(33):48-55, January 2020. BibTeX

@article{10.5120/ijca2020919821,
	author = {Bolanle F. Oladejo and Oladejo Olajide A.},
	title = {Enriching Quality of Maternal Health Care through Machine Learning},
	journal = {International Journal of Computer Applications},
	issue_date = {January 2020},
	volume = {177},
	number = {33},
	month = {Jan},
	year = {2020},
	issn = {0975-8887},
	pages = {48-55},
	numpages = {8},
	url = {http://www.ijcaonline.org/archives/volume177/number33/31118-2020919821},
	doi = {10.5120/ijca2020919821},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Pregnancy outcomes rank the most pressing reproductive health problems in the world globally. Most maternal complications and deaths occur as a result of insufficient quality of care during pregnancy and labour. In most parts of the developing world, access to quality health care is limited and people depend on the health care providers who have limited training. Advancements in medical technology have drastically increased the quantity of data available in the healthcare industry, ranging from patient reports and genomic data to electronic medical records. These data can provide a wide scope of insights into patient cases for prevention and cure on health issues. Thus, this research aimed at designing a machine learning-based decision support system for maternal health care. The system is designed using Unified Modeling Language (UML) tools and a multi-class Support Vector Machine (SVM) was developed for the Decision Support System for Maternity Health Care (DSSMC). A Web-based DSSMC was developed and tested to facilitate automatic diagnosis of patient and to solve the problem of human error and bias.

References

  1. Abderrazak S., Amina N., Abdelkamel T., Ramtani T., Ouhab A.(2017). Decision Support System for Health Care Resources Allocation.
  2. Addai I. Demographic and sociocultural factors influencing use of maternalhealth services in Ghana. Afr J Reprod Health. 1998;2(1):73–80.17.
  3. Agrawal A. (2002). Return on investment analysis for a computer-based patient record in the outpatient clinic setting. Journal of the Association for Academic Minority Physicians: the official publication of the Association for Academic Minority Physicians, 13(3), 61-65.
  4. Chaudy Yaelle, Connolly Thomas, Magowan Brian & Soflano Mario. (2017). SAFER Maternity: A Clinical Decision Support System with an Authoring Tool for Clinicians.
  5. Christo El Morr, Julien Subercaze(2010), knowledge management in healthcare. Yorku Components of information systems, https://www.britannica.com/list/5-components-of-information-systems(accessed Dec, 2018).
  6. Garg AX, Adhikari NKJ, McDonald H, et al. Effects of Computerized Clinical Decision Support Systems on Practitioner Performance and Patient Outcomes: A Systematic Review. JAMA. 2005;293(10):1223–1238. doi:10.1001/jama.293.10.1223
  7. Karel Fuka, Elina Syrjanen, Rudolf Hanka. (2019). Knowledge Management in Healthcare.Researchagate.
  8. Kathrin C., Azeem M., David W.B., AAziz S. (2012). Computerised Decision Support Systems for HealthCare Professionals: An interpretative Review.researchgate.
  9. Kawamoto, Kensaku & Houlihan, Caitlin & Balas, Andrew & Lobach, David. (2005). Improving Clinical Practice Using Clinical Decision Support Systems: A Systematic Review of Trials to Identify Features Critical to Success. BMJ (Clinical research ed.). 330. 765. 10.1136/bmj.38398.500764.8F.
  10. Leila Shahmoradi., Reza safadari., Worku Jimma(2017), knowledge management implememtation and the tools utilized in healthcare for evidence-based decision making: A systematic Review. Ncbi.
  11. Menachemi N. and Brooks R. G. (2006). Reviewing the benefits and costs of electronic health records and associated patient safety technologies. Journal of Medical Systems, 30(3), 159-168.
  12. Nor;Ashikin Ali, Alexei Tretiakov, Dick Whiddett, Ingaa Hunter (2017), knowledge management systems success in healthcare:Leadership matters.sciencedirect.
  13. O.A. Bolarinwa, A.G. Salaudeen, T.M. Akande(2012), overview of knowledge management applications in health care delivery of developing countries.savap.
  14. Okolocha, C.; Chiwuzie, J; Braimoh, S; Unuigbe, J. and Olumeko, P. (1998). “Socio-cultural factors in Maternal Morbidity and Mortality: A Study of A Semi-Urban Community in Southern Nigeria”. Journal of Epidemiology and Community Health, 52(5): 293 – 297.
  15. Paydar, Khadijeh & Rostam Niakan Kalhori, Sharareh & Akbarian, Mahmoud & Sheikhtaheri, Abbas. (2016). A clinical decision support system for prediction of pregnancy outcome in pregnant women with systemic lupus erythematosus. International Journal of Medical Informatics. 97. 10.1016/j.ijmedinf.2016.10.018.
  16. Population Reference Bureau. (2002). Making Motherhood Safer: Overcoming Obstacles on the Pathway to Care. Washington: Population Reference Bureau.
  17. Salter, C., Johnston, H.B., and Hengen, N. (1997). Care for post abortion Complications: Saving Women’s lives. Population Reports. Series L, No. 10. Baltimore: JohnsHopkinsSchool of Public Health. Vol. Xxv (1).
  18. Seenivasan P., Sujatha S., Caroline P. K., Saurav D., Srinivasan R., Satya S. P. and Gurlivleen S. G. (2014). Medical Decision Support Technology for Better Antenatal Care of the Mother, Under-Five Child Survival and Child Health in Rural India.
  19. Stroetmann, B., & Aisenbrey, A. (2012). Medical Knowledge Management in Healthcare Industry.
  20. Van J.B and Musen M.A (1997). Handbook of medical informatics.
  21. Vinay R. Rao (2018). How data becomes knowledge, part 1. IBM.
  22. Wall, L.L. (1998). Dead Mothers and Injured Wives: The Social Context of Maternal Morbidity and Mortality among the Hausa of Northern Nigeria. Studies in Family Planning, 29 (4).
  23. Wagholikar, Kavishwar & Maclaughlin, Kathy & Kastner, Thomas & Casey, Petra & Henry, Michael & Greenes, Robert & Liu, Hongfang & Chaudhry, Rajeev. (2013). Formative evaluation of the accuracy of a clinical decision support system for cervical cancer screening. Journal of the American Medical Informatics Association : JAMIA. 20. 10.1136/amiajnl-2013-001613.
  24. Weed L.L. (1997), Neew connections between medical knowledge and patient care, British medical journal, No. 315, p.231-235.
  25. WHO. (2000). Safe Motherhood: A Newsletter of World Wide Activity. Issue 28(1).
  26. Yaser Hasan Al-mamary, Alina Shamsuddin & Nor Aziati (2014), the Role of Different types od information systems in business organizations: A review, International journal of research vol 1, issue-7.

Keywords

Maternal Health, Diagnosis, Decision Support System, Machine Learning, Knowledge Management.