Cover
Vol. 17 No. 2 (2021)

Published: December 31, 2021

Pages: 120-128

Original Article

Towards for Designing Intelligent Health Care System Based on Machine Learning

Nada Ali Noori 1 Ali A. Yassin
1Department of Computer science, Education College for Pure Sciences, University of Basrah, Basrah, 61004, Iraq
History
  • Received: July 17, 2021
  • Revised: August 29, 2021
  • Accepted: September 9, 2021
  • Available Online: September 10, 2021
DOI

https://doi.org/10.37917/ijeee.17.2.14

Abstract

Health Information Technology (HIT) provides many opportunities for transforming and improving health care systems. HIT enhances the quality of health care delivery, reduces medical errors, increases patient safety, facilitates care coordination, monitors the updated data over time, improves clinical outcomes, and strengthens the interaction between patients and health care providers. Living in modern large cities has a significant negative impact on people's health, for instance, the increased risk of chronic diseases such as diabetes. According to the rising morbidity in the last decade, the number of patients with diabetes worldwide will exceed 642 million in 2040, meaning that one in every ten adults will be affected. All the previous research on diabetes mellitus indicates that early diagnoses can reduce death rates and overcome many problems. In this regard, machine learning (ML) techniques show promising results in using medical data to predict diabetes at an early stage to save people's lives. In this paper, we propose an intelligent health care system based on ML methods as a real-time monitoring system to detect diabetes mellitus and examine other health issues such as food and drug allergies of patients. The proposed system uses five machine learning methods: K-Nearest Neighbors, Naïve Bayes, Logistic Regression, Random Forest, and Support Vector Machine (SVM). The system selects the best classification method with high accuracy to optimize the diagnosis of patients with diabetes. The experimental results show that in the proposed system, the SVM classifier has the highest accuracy of 83%.

Keywords

References

  1. G. S. Nelson, T. Technologies, and C. Hill, “A Practical Guide to Healthcare Data : Tips , traps and techniques,” Think. data, vol. 1, no. August, pp. 1–20, 2017.
  2. C. H. Tsai, A. Eghdam, N. Davoody, G. Wright, S. Flowerday, and S. Koch, “Effects of Electronic Health Record Implementation and Barriers to Adoption and Use: A Scoping Review and Qualitative Analysis of the Content,” Life, vol. 10, no. 12, pp. 1–27, 2020, doi: 10.3390/life10120327.
  3. L. Akhu-Zaheya, R. Al-Maaitah, and S. Bany Hani, “Quality of nursing documentation: Paper-based health records versus electronic-based health records,” J. Clin. Nurs., vol. 27, no. 3–4, pp. e578–e589, 2018, doi: 10.1111/jocn.14097.
  4. L. Waithera, J. Muhia, and R. Songole, “Impact of Electronic Medical Records on Healthcare Delivery in Kisii Teaching and Referral Hospital,” Med. Clin. Rev., vol. 03, no. 04, pp. 1–7, 2017, doi: 10.21767/2471- 299x.1000062.
  5. D. Meetoo, “Chronic diseases: the silent global epidemic.,” Br. J. Nurs., vol. 17, no. 21, pp. 1320–1325, 2008, doi: 10.12968/bjon.2008.17.21.31731.
  6. M. Güemes, S. A. Rahman, and K. Hussain, “What is a normal blood glucose?,” Arch. Dis. Child., vol. 101, no. 6, pp. 569–574, 2016, doi: 10.1136/archdischild-2015- 308336.
  7. A. Khan and S. Khan, “Causes, Complications and Management of Diabetes Mellitus,” no. August, 2017.
  8. R. Goldenberg and Z. Punthakee, “Definition, Classification and Diagnosis of Diabetes, Prediabetes and Metabolic Syndrome,” Can. J. Diabetes, vol. 37, no. SUPPL.1, pp. 8–11, 2013, doi: 10.1016/j.jcjd.2013.01.011.
  9. M. Abusaib et al., “Iraqi Experts Consensus on the Management of Type 2 Diabetes/Prediabetes in Adults,” Clin. Med. Insights Endocrinol. Diabetes, vol. 13, 2020, doi: 10.1177/1179551420942232.
  10. S. Ellahham, “Artificial Intelligence: The Future for Diabetes Care,” Am. J. Med., vol. 133, no. 8, pp. 895–900, 2020, doi: 10.1016/j.amjmed.2020.03.033.
  11. M. A. Jabbar, S. Samreen, and R. Aluvalu, “The future of health care: Machine learning,” Int. J. Eng. Technol., vol. 7, no. 4, pp. 23–25, 2018, doi: 10.14419/ijet.v7i4.6.20226.
  12. P. Pundir, V. Gomanse, and N. Krishnamacharya, “Classification and Prediction techniques using Machine Learning for Anomaly Detection .,” Pdfs.Semanticscholar.Org, vol. 1, no. 4, pp. 1716–1722, 2011, [Online]. Available: https://pdfs.semanticscholar.org/267d/0ba8de46c022bf9ff d6af4cd0c4b403798ea.pdf.
  13. S. B. Imandoust and M. Bolandraftar, “Application of K-Nearest Neighbor ( KNN ) Approach for Predicting Economic Events : Theoretical Background,” Int. J. Eng. Res. Appl., vol. 3, no. 5, pp. 605–610, 2013.
  14. Z. Zhang, “Introduction to machine learning: K-nearest neighbors,” Ann. Transl. Med., vol. 4, no. 11, 2016, doi: 10.21037/atm.2016.03.37.
  15. J. Kazmierska and J. Malicki, “Application of the Naïve Bayesian Classifier to optimize treatment decisions,” Radiother. Oncol., vol. 86, no. 2, pp. 211–216, 2008, doi: 10.1016/j.radonc.2007.10.019.
  16. D. Berrar, “Bayes’ theorem and naive bayes classifier,” Encycl. Bioinforma. Comput. Biol. ABC Bioinforma., vol. 1–3, no. January 2018, pp. 403–412, 2018, doi: 10.1016/B978-0-12-809633-8.20473-1.
  17. S. Domínguez-Almendros, N. Benítez-Parejo, and A. R. Gonzalez-Ramirez, “Logistic regression models,” Allergol. Immunopathol. (Madr)., vol. 39, no. 5, pp. 295– 305, 2011, doi: 10.1016/j.aller.2011.05.002.
  18. J. You, S. A. S. van der Klein, E. Lou, and M. J. Zuidhof, “Application of random forest classification to predict daily oviposition events in broiler breeders fed by precision feeding system,” Comput. Electron. Agric., vol. 175, no. June, p. 105526, 2020, doi: 10.1016/j.compag.2020.105526.
  19. Y. J. Ccoicca, “Applications of Support Vector Machines in the Exploratory Phase of Petroleum and Natural Gas: a Survey,” Int. J. Eng. Technol., vol. 2, no. 2, p. 113, 2013, doi: 10.14419/ijet.v2i2.834.
  20. N. Barakat, A. P. Bradley, and M. N. H. Barakat, “Intelligible support vector machines for diagnosis of diabetes mellitus,” IEEE Trans. Inf. Technol. Biomed., vol. 14, no. 4, pp. 1114–1120, 2010, doi: 10.1109/TITB.2009.2039485.
  21. L. Han, S. Luo, J. Yu, L. Pan, and S. Chen, “Rule extraction from support vector machines using ensemble learning approach: An application for diagnosis of diabetes,” IEEE J. Biomed. Heal. Informatics, vol. 19, no. 2, pp. 728–734, 2015, doi: 10.1109/JBHI.2014.2325615.
  22. R. G. Brereton and G. R. Lloyd, “Support Vector Machines for classification and regression,” Analyst, vol. 135, no. 2, pp. 230–267, 2010, doi: 10.1039/b918972f.
  23. W. Xu, J. Zhang, Q. Zhang, and X. Wei, “Risk prediction of type II diabetes based on random forest model,” Proc. 3rd IEEE Int. Conf. Adv. Electr. Electron. Information, Commun. Bio-Informatics, AEEICB 2017, pp. 382–386, 2017, doi: 10.1109/AEEICB.2017.7972337. Noori & Yassin
  24. M. Komi, J. Li, Y. Zhai, and Z. Xianguo, “Application of data mining methods in diabetes prediction,” 2017 2nd Int. Conf. Image, Vis. Comput. ICIVC 2017, no. S Ix, pp. 1006–1010, 2017, doi: 10.1109/ICIVC.2017.7984706.
  25. S. Perveen, M. Shahbaz, A. Guergachi, and K. Keshavjee, “Performance Analysis of Data Mining Classification Techniques to Predict Diabetes,” Procedia Comput. Sci., vol. 82, no. March, pp. 115–121, 2016, doi: 10.1016/j.procs.2016.04.016.
  26. M. Mounika, S. Suganya, B. Vijayashanthi, and S. Anand, “Predictive analysis of diabetic treatment using classification algorithm,” Ijcsit, vol. 6, no. 3, pp. 2502– 2505, 2015, [Online]. Available: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1 .734.9118&rep=rep1&type=pdf.
  27. A. Mujumdar and V. Vaidehi, “Diabetes Prediction using Machine Learning Algorithms,” Procedia Comput. Sci., vol. 165, pp. 292–299, 2019, doi: 10.1016/j.procs.2020.01.047.
  28. N. Sneha and T. Gangil, “Analysis of diabetes mellitus for early prediction using optimal features selection,” J. Big Data, vol. 6, no. 1, 2019, doi: 10.1186/s40537-019- 0175-6.
  29. R. Deo and S. Panigrahi, “Performance Assessment of Machine Learning Based Models for Diabetes Prediction,” 2019 IEEE Healthc. Innov. Point Care Technol. HI-POCT 2019, no. 11, pp. 147–150, 2019, doi: 10.1109/HI- POCT45284.2019.8962811.
  30. N. P. Tigga and S. Garg, “Prediction of Type 2 Diabetes using Machine Learning Classification Methods,” Procedia Comput. Sci., vol. 167, no. 01, pp. 706–716, 2020, doi: 10.1016/j.procs.2020.03.336.
  31. R. Meza-Palacios, A. A. Aguilar-Lasserre, E. L. Ureña- Bogarín, C. F. Vázquez-Rodríguez, R. Posada-Gómez, and A. Trujillo-Mata, “Development of a fuzzy expert system for the nephropathy control assessment in patients with type 2 diabetes mellitus,” Expert Syst. Appl., vol. 72, no. February 2019, pp. 335–343, 2017, doi: 10.1016/j.eswa.2016.10.053.
  32. S. Bashir, U. Qamar, F. H. Khan, and L. Naseem, “HMV: A medical decision support framework using multi-layer classifiers for disease prediction,” J. Comput. Sci., vol. 13, pp. 10–25, 2016, doi: 10.1016/j.jocs.2016.01.001.
  33. https://www.who.int/health-topics/diabetes
  34. M. Z. Al-Faiz, A. A. Ali, and A. H. Miry, “A k-nearest neighbor based algorithm for human arm movements recognition using EMG signals,” Iraqi Journal for Electrical and Electronic Engineering, vol. 6, no. 2, pp. 159–167, 2010, doi: 10.37917/ijeee.6.2.12.
  35. https://www.kaggle.com/uciml/pima-indians-diabetes- database