International Journal of Information Technology and Computer Science (IJITCS)
IJITCS Vol. 16, No. 6, 8 Dec. 2024
Cover page and Table of Contents: PDF (size: 1104KB)
Enhancing Drug Recommender System for Peptic Ulcer Treatment
PDF (1104KB), PP.15-26
Views: 0 Downloads: 0
Author(s)
Theresa O. Omodunbi
1,*
Grace E. Alilu
2
Kennedy O. Obohwemu
3
Rhoda N. Ikono
1
Index Terms
Drug Recommender System, Peptic Ulcer Disease, Collaborative Filtering, Knowledge-based Filtering, Decision Support Systems
Abstract
Drug Recommender Systems (DRS) streamline prescription process and contribute to better healthcare. Hence, this study developed a DRS that recommends appropriate drug(s) for the treatment of an ailment using Peptic Ulcer Disease (PUD) as a case study. Patients’ and drug data were elicited from MIMIC-IV and Drugs.com, respectively. These data were analysed and used in the design of the DRS model, which was based on the hybrid recommendation approach (combining the clustering algorithm, the Collaborative Filtering approach (CF), and the Knowledge-Based Filtering approach (KBF)). The factors that were considered in recommending appropriate drugs were age, gender, body weight, allergies, and drug interactions. The model designed was implemented in Python programming language with the Flask framework for web development and Visual Studio Code as the Integrated Development Environment. The performance of the system was evaluated using Precision, Recall, Accuracy, Root Mean Squared Error (RMSE) and usability test. The evaluation was carried out in two phases. Firstly, the CF component was evaluated by splitting the dataset from MIMIV-IV into a 70% (60,018) training set and a 30% (25,722) test set. This resulted in a precision score of 85.48%, a recall score of 85.58%, and a RMSE score of 0.74. Secondly, the KBF component was evaluated using 30 different cases. The evaluation for this was computed manually by comparing the recommendation results from the system with those of an expert. This resulted in a precision of 77%, a recall of 83%, an accuracy of 81% and an RMSE of 0.24. The results from the usability test showed a high percentage of performance of the system. The addition of the KBF reduced the error rate between actual recommendations and predicted recommendations. So, the system had a high ability to recommend appropriate drug(s) for PUD.
Cite This Paper
Theresa O. Omodunbi, Grace E. Alilu, Kennedy O. Obohwemu, Rhoda N. Ikono, "Enhancing Drug Recommender System for Peptic Ulcer Treatment", International Journal of Information Technology and Computer Science(IJITCS), Vol.16, No.6, pp.15-26, 2024. DOI:10.5815/ijitcs.2024.06.02
Reference
[1]K. Zarour, M. O. Fetni, and S. Belagrouz, “Towards Electronic Prescription System in a Developing Country Cooperative information systems View project ABAH: Agent-Based Architecture for Homecare View project,” pp. 56–67, 2021, [Online]. Available: https://www.researchgate.net/publication/350709082
[2]E. I. Umegbolu, “Peptic ulcer disease in school children aged 2-11 years in Southeast Nigeria,” Int J Res Med Sci, vol. 10, no. 5, p. 1007, Apr. 2022, doi: 10.18203/2320-6012.ijrms20221169.
[3]X. Xie, K. Ren, Z. Zhou, C. Dang, and H. Zhang, “The global, regional and national burden of peptic ulcer disease from 1990 to 2019: a population-based study,” BMC Gastroenterol, vol. 22, no. 1, Dec. 2022, doi: 10.1186/s12876-022-02130-2.
[4]S. Bidokumo Zibima, J. Imawaigha Oniso, K. Belibodei Wasini, J. Chidinma Ogu, and C. Author, “Prevalence Trends and Associated Modifiable Risk Factors of Peptic Ulcer Disease among Students in a University Community South-South Nigeria,” 2020. [Online]. Available: www.ijhsr.org
[5]E. Ray-Offor and K. A. Opusunju, “Current status of peptic ulcer disease in port harcourt metropolis, nigeria,” Afr Health Sci, vol. 20, no. 3, pp. 1446–1451, Sep. 2020, doi: 10.4314/ahs.v20i3.50.
[6]M. Ahmed, “Peptic Ulcer Disease,” in Management of Digestive Disorders, IntechOpen, 2019, pp. 1–20. doi: 10.5772/intechopen.8 6652.
[7]A. Mark Fendrick, R. T. Forsch, R. Van Harrison, J. M. Scheiman, C. J. Standiford, and L. A. Green, “University of Michigan Guidelines for Health System Clinical Care Peptic Ulcer Guideline Team UMMC Guidelines Oversight Team Peptic Ulcer Disease,” May 2005.
[8]T. Kamada et al., “Evidence-based clinical practice guidelines for peptic ulcer disease 2020,” Journal of Gastroenterology, vol. 56, no. 4. Springer Japan, pp. 303–322, Apr. 01, 2021. doi: 10.1007/s00535-021-01769-0.
[9]W. Andrew and R. Robert, “Gastric Ulcer,” Stat Pearls. Accessed: Jun. 28, 2024. [Online]. Available: https://www.ncbi.nlm.nih.gov/books/NBK537128/
[10]L. ME, R.-P. M, P. I, and R. L, “Peptic Ulcer Disease,” Journal of Gastroenterology and Hepatobiliary Disorders, vol. 01, no. 01, Dec. 2015, doi: 10.19104/jghd.2015.105.
[11]B. Stark, C. Knahl, M. Aydin, and K. Elish, “A Literature Review on Medicine Recommender Systems,” vol. 10, no. 8, pp. 6–13, 2019.
[12]Q. Zhang, G. Zhang, J. Lu, and D. Wu, “A framework of hybrid recommender system for personalized clinical prescription,” in Proceedings - The 2015 10th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2015, Institute of Electrical and Electronics Engineers Inc., Jan. 2016, pp. 189–195. doi: 10.1109/ISKE.2015.98.
[13]S. Garg, “Drug Recommendation System based on Sentiment Analysis of Drug Reviews using Machine Learning,” Mar. 2021, doi: 10.1109/Confluence51648.2021.9377188.
[14]A. A. R. Kamuhabwa and S. Kisoma, “Factors influencing prescribing practices of medical practitioners in public and private health facilities in dar es salaam, Tanzania,” Tropical Journal of Pharmaceutical Research, vol. 14, no. 11, pp. 2107–2113, Nov. 2015, doi: 10.4314/tjpr.v14i11.22.
[15]O. R. S. Rao, “Factors influencing Drug Prescription Behavior of Physicians in India,” no. August, 2019.
[16]J. Ayrine, I. H. Muhammed, and V. Vasudevan, “Medication recommendation system based on clinical documents,” in Proceedings - 2016 International Conference on Information Science, ICIS 2016, Institute of Electrical and Electronics Engineers Inc., Feb. 2017, pp. 180–184. doi: 10.1109/INFOSCI.2016.7845323.
[17]T. V. N. Rao, A. Unnisa, and K. Sreni, “Medicine Recommendation System Based On Patient Reviews,” vol. 9, no. 02, 2020.
[18]T. O. Omodunbi, G. E. Alilu, and R. N. Ikono, “Drug Recommender Systems: A Review of State- of-the-Art Algorithms,” in 2022 5th Information Technology Conference for Education and Development (ITED), IEEE XPLORE, 2022, pp. 1–8.
[19]Y. Bao and X. Jiang, “An intelligent medicine recommender system framework,” in Proceedings of the 2016 IEEE 11th Conference on Industrial Electronics and Applications, ICIEA 2016, Institute of Electrical and Electronics Engineers Inc., Oct. 2016, pp. 1383–1388. doi: 10.1109/ICIEA.2016.7603801.
[20]Hossain Deloar, Azam Shafiul, Ali Jahan, and Hakilo Sabit, “Drugs Rating Generation and Recommendation from Sentiment Analysis of Drug Reviews using Machine Learning,” in 2020 Emerging Technology in Computing, Communication and Electronics (ETCCE), IEEE XPLORE, 2020, pp. 1–6. doi: 10.1109/ETCCE51779.2020.9350868/20/$31.00.
[21]V. A. Goyal, D. J. Parmar, N. I. Joshi, and P. K. Champanerkar, “Medicine Recommendation System,” pp. 1658–1662, 2020.
[22]S. Bhat and K. Aishwarya, “Marketed Pharmaceutical Drugs,” pp. 2107–2111, 2013.
[23]T. B. Adetoba and A. N. Yekini, “A Comprehensive Study of Recommender Systems Prospects and Challenges,” Int J Sci Eng Res, vol. 6, no. 8, pp. 699–714, 2015, [Online]. Available: http://www.ijser.org
[24]M. Uta et al., “Knowledge-based recommender systems: overview and research directions,” Frontiers in Big Data, vol. 7. Frontiers Media SA, pp. 1–19, 2024. doi: 10.3389/fdata.2024.1304439.
[25]P. K. Biswas and S. Liu, “A Hybrid Recommender System for Recommending Smartphones to Prospective Customers,” Expert Syst Appl, vol. 208, pp. 1–23, 2020.
[26]M. Sharma and S. Mann, “A Survey of Recommender Systems: Approaches and Limitations,” International Journal of Innovations in Engineering and Technology Special Issue-ICAECE, 2013.
[27]Z. Fayyaz, M. Ebrahimian, D. Nawara, A. Ibrahim, and R. Kashef, “Recommendation systems: Algorithms, challenges, metrics, and business opportunities,” Applied Sciences (Switzerland), vol. 10, no. 21, pp. 1–20, Nov. 2020, doi: 10.3390/app10217748.
[28]K. Kareem, “As doctors emigrate, Nigerians are left with four doctors to every 10,000 patients,” Dataphyte.
[29]I. P. Gambo and A. H. Soriyan, “ICT Implementation in the Nigerian Healthcare System,” T Professional, vol. 19, no. 2, pp. 12–15, 2017, doi: 10.1109/MITP.2017.21.
[30]I. Gambo, E. O. Ayegbusi, O. Abioye, T. Omodunbi, R. Ikono, and K. Olufokunbi, “Design Specification for an M-Health Solution to Improve Antenatal Care,” in Advancing Health Education with Telemedicine, 2021, pp. 41–79. doi: 10.4018/978-1-7998-8783-6.ch003.
[31]S. Bankhele, A. Mhaske, S. Bhat, and S. V. Shinde, “A Diabetic Healthcare Recommendation System,” International Journal of Computer Applications, vol. 167, no. 5, pp. 14-18, 2017.
[32]A. Goldberger, L. Amaral, L. Glass, J. Hausdorff, P. C. Ivanov, R. Mark, ... and H. E. Stanley, “PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals”, vol 101, no. 23, 2000, e215–e220.
[33]A. Johnson, L. Bulgarelli, T. Pollard, S. Horng, L. A. Celi and R. Mark, “MIMIC-IV (version 2.2)”, PhysioNet, 2023, doi: 10.13026/6mm1-ek67.
[34]Drugs.com, “Drugs & Medications A to Z”, Drugs.com Database, 2023, https://www.drugs.com/drug_information.html.
[35]L. F. Morales, P. Valdiviezo-Diaz, R. Reátegui, and L. Barba-Guaman, “Drug Recommendation System for Diabetes Using a Collaborative Filtering and Clustering Approach: Development and Performance Evaluation,” Journal of Medical Internet Research, vol. 24 no. 7, pp. 1-9, 2022, https://doi.org/10.2196/37233