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Applied Machine Learning Techniques for Chronic Disease Treatment Default Prediction and its Potential Benefits for Patient Outcome: A Case Series Study Approach

by Michael Owusu-Adjei, Joseph Manasseh Opong, Apenteng Samuel
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 9
Year of Publication: 2024
Authors: Michael Owusu-Adjei, Joseph Manasseh Opong, Apenteng Samuel
10.5120/ijca2024923443

Michael Owusu-Adjei, Joseph Manasseh Opong, Apenteng Samuel . Applied Machine Learning Techniques for Chronic Disease Treatment Default Prediction and its Potential Benefits for Patient Outcome: A Case Series Study Approach. International Journal of Computer Applications. 186, 9 ( Feb 2024), 37-45. DOI=10.5120/ijca2024923443

@article{ 10.5120/ijca2024923443,
author = { Michael Owusu-Adjei, Joseph Manasseh Opong, Apenteng Samuel },
title = { Applied Machine Learning Techniques for Chronic Disease Treatment Default Prediction and its Potential Benefits for Patient Outcome: A Case Series Study Approach },
journal = { International Journal of Computer Applications },
issue_date = { Feb 2024 },
volume = { 186 },
number = { 9 },
month = { Feb },
year = { 2024 },
issn = { 0975-8887 },
pages = { 37-45 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number9/applied-machine-learning-techniques-for-chronic-disease-treatment-default-prediction-and-its-potential-benefits-for-patient-outcome-a-case-series-study-approach/ },
doi = { 10.5120/ijca2024923443 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-29T03:28:39.350290+05:30
%A Michael Owusu-Adjei
%A Joseph Manasseh Opong
%A Apenteng Samuel
%T Applied Machine Learning Techniques for Chronic Disease Treatment Default Prediction and its Potential Benefits for Patient Outcome: A Case Series Study Approach
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 9
%P 37-45
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In medical consideration for missing diagnosis and false diagnosis of disease types are important clinical considerations for disease treatment decisions. Effect and impact of disease types especially on others forms the basis for critical clinical decisions. Impact and consequences varies across disease types especially for communicable and -non-communicable diseases. Increasing use of predictive techniques owingto high use of connected internet of things devices in healthcare provides sufficient opportunity for potential benefit assessment of predictive modeling impact on disease treatment management. Effective and efficient management of -non-communicable diseases such as hypertension is hampered in part by instances of multiple forms of its occurrence in patients leading to treatment management complications. Probing predictive modeling effectand implications for clinical decisions to enhance patient treatment outcome provides important evidence-based justifications for its use in healthcare systems. Effective predictive technique use issignificantly dependenton areas of its application and the consequences of error for its use in context.

References
  1. T. J. Stein, “Critical thinking in clinical practice,” Child. Youth Serv. Rev., vol. 13, no. 5–6, pp. 441–443, 1991, doi: 10.1016/0190-7409(91)90032-d.
  2. S. Vollmer et al., “Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness,” BMJ, vol. 368, pp. 1–12, 2020, doi: 10.1136/bmj.l6927.
  3. S. A. Alowais et al., “Revolutionizing healthcare: the role of artificial intelligence in clinical practice,” BMC Med. Educ., vol. 23, no. 1, pp. 1–15, 2023, doi: 10.1186/s12909-023-04698-z.
  4. R. Kang, E. M. Rantanen, and E. A. Youngstrom, “Machine Learning in Healthcare: Two Case Studies,” Proc. Hum. Factors Ergon. Soc. Annu. Meet., vol. 66, no. 1, pp. 774–778, 2022, doi: 10.1177/1071181322661518.
  5. L. Moja et al., “Effectiveness of a Hospital-Based Computerized Decision Support System on Clinician Recommendations and Patient Outcomes: A Randomized Clinical Trial,” JAMA Netw. Open, vol. 2, no. 12, pp. 1–16, 2019, doi: 10.1001/jamanetworkopen.2019.17094.
  6. A. Choudhury and C. Allen, “Artificially Intelligent? Machine Learning in Healthcare and Why It May Not Be As Advanced As You Think,” Patient Saf., vol. 5, no. 2, pp. 76–83, 2023, doi: 10.33940/001c.77632.
  7. P. M. Rodrigues, J. P. Madeiro, and J. A. L. Marques, “Enhancing Health and Public Health through Machine Learning: Decision Support for Smarter Choices,” Bioengineering, vol. 10, no. 7, pp. 1–5, 2023, doi: 10.3390/bioengineering10070792.
  8. A. Zhang, L. Xing, J. Zou, and J. C. Wu, “Shifting machine learning for healthcare from development to deployment and from models to data,” Nat. Biomed. Eng., vol. 6, no. 12, pp. 1330–1345, 2022, doi: 10.1038/s41551-022-00898-y.
  9. D. B. Olawade, O. J. Wada, A. C. David-Olawade, E. Kunonga, O. Abaire, and J. Ling, “Using artificial intelligence to improve public health: a narrative review,” Front. Public Heal., vol. 11, no. October, pp. 1–9, 2023, doi: 10.3389/fpubh.2023.1196397.
  10. Z. Wen and H. Huang, “The potential for artificial intelligence in healthcare,” J. Commer. Biotechnol., vol. 27, no. 4, pp. 217–224, 2022, doi: 10.5912/jcb1327.
  11. T. Panch, P. Szolovits, and R. Atun, “Artificial intelligence, machine learning and health systems,” J. Glob. Health, vol. 8, no. 2, pp. 1–8, 2018, doi: 10.7189/jogh.08.020303.
  12. F. Leonard, D. O’Sullivan, J. Gilligan, N. O’Shea, and M. J. Barrett, “Supporting clinical decision making in the emergency department for paediatric patients using machine learning: A scoping review protocol,” PLoS One, vol. 18, no. 11, p. e0294231, 2023, doi: 10.1371/journal.pone.0294231.
  13. S. Mudalige, D. Alexis, and C. Jayatilake, “Involvement of Machine Learning Tools in Healthcare,” vol. 2021, 2021.
  14. B. Shamreen Ahamed, M. S. Arya, and A. O. Nancy, “Diabetes Mellitus Disease Prediction Using Machine Learning Classifiers and Techniques Using the Concept of Data Augmentation and Sampling,” Lect. Notes Networks Syst., vol. 516, pp. 401–413, 2023, doi: 10.1007/978-981-19-5221-0_40.
  15. J. de la Torre, J. Marin, S. Ilarri, and J. J. Marin, “Applying machine learning for healthcare: A case study on cervical pain assessment with motion capture,” Appl. Sci., vol. 10, no. 17, 2020, doi: 10.3390/app10175942.
  16. R. Gyebi et al., “Prediction of measles patients using machine learning classifiers: a comparative study,” Bull. Natl. Res. Cent., vol. 47, no. 1, 2023, doi: 10.1186/s42269-023-01079-w.
  17. R. S. Hu, A. E. L. Hesham, and Q. Zou, “Machine Learning and Its Applications for Protozoal Pathogens and Protozoal Infectious Diseases,” Front. Cell. Infect. Microbiol., vol. 12, no. April, pp. 1–17, 2022, doi: 10.3389/fcimb.2022.882995.
  18. A. Pfob et al., “Identification of breast cancer patients with pathologic complete response in the breast after neoadjuvant systemic treatment by an intelligent vacuum-assisted biopsy,” Eur. J. Cancer, vol. 143, pp. 134–146, Jan. 2021, doi: 10.1016/J.EJCA.2020.11.006.
  19. R. Haneef et al., “Use of artificial intelligence for public health surveillance: a case study to develop a machine Learning-algorithm to estimate the incidence of diabetes mellitus in France,” Arch. Public Heal., vol. 79, no. 1, pp. 1–13, 2021, doi: 10.1186/s13690-021-00687-0.
  20. M. Sharma, “Data Mining Prediction Techniques in Health Care Sector,” J. Phys. Conf. Ser., vol. 2267, no. 1, 2022, doi: 10.1088/1742-6596/2267/1/012157.
  21. “Python Logistic Regression Tutorial with Sklearn & Scikit | DataCamp.” https://www.datacamp.com/tutorial/understanding-logistic-regression-python (accessed Dec. 29, 2023).
  22. “Scikit-learn SVM Tutorial with Python (Support Vector Machines) | DataCamp.” https://www.datacamp.com/tutorial/svm-classification-scikit-learn-python (accessed Dec. 29, 2023).
  23. “Random Forest Classifier using Scikit-learn - GeeksforGeeks.” https://www.geeksforgeeks.org/random-forest-classifier-using-scikit-learn/ (accessed Dec. 29, 2023).
  24. “Gradient Boosting with Scikit-Learn, XGBoost, LightGBM, and CatBoost - MachineLearningMastery.com.” https://machinelearningmastery.com/gradient-boosting-with-scikit-learn-xgboost-lightgbm-and-catboost/ (accessed Dec. 29, 2023).
  25. “A Comprehensive Guide to Using Scikit-Learn’s MLPClassifier - AITechTrend.” https://aitechtrend.com/a-comprehensive-guide-to-using-scikit-learns-mlpclassifier/ (accessed Dec. 29, 2023).
  26. “Getting Started With The Extra Trees Algorithm In Python 2023.” https://hands-on.cloud/getting-started-with-the-extra-trees-algorithm-in-python/ (accessed Dec. 29, 2023).
  27. “Decision Tree Classifier with Sklearn in Python • datagy.” https://datagy.io/sklearn-decision-tree-classifier/ (accessed Dec. 29, 2023).
  28. “Bagging Classifier Python Code Example.” https://vitalflux.com/bagging-classifier-python-code-example/ (accessed Dec. 29, 2023).
  29. “K-nearest Neighbors in Scikit-learn - KDnuggets.” https://www.kdnuggets.com/2022/07/knearest-neighbors-scikitlearn.html (accessed Dec. 29, 2023).
  30. “Linear Discriminant Analysis Made Simple & How To Tutorial.” https://spotintelligence.com/2023/11/07/linear-discriminant-analysis-lda/ (accessed Dec. 29, 2023).
  31. P. Mohanty, L. Patnaik, G. Nayak, and A. Dutta, “Gender difference in prevalence of hypertension among Indians across various age-groups: a report from multiple nationally representative samples,” BMC Public Health, vol. 22, no. 1, pp. 1–10, 2022, doi: 10.1186/s12889-022-13949-5.
  32. P. Chhabra et al., “Gender-specific factors associated with hypertension among women of childbearing age: Findings from a nationwide survey in India,” Front. Cardiovasc. Med., vol. 9, no. December, pp. 1–9, 2022, doi: 10.3389/fcvm.2022.999567.
  33. T. J. Toney-Butler and J. M. Thayer, “Nursing Process,” Fundam. Nurs. Made Incred. Easy! Second Ed., p. 4, Apr. 2023, doi: 10.5005/jp/books/14252_4.
Index Terms

Computer Science
Information Sciences

Keywords

Communicable -non-communicable diseases prediction effect impact treatment performance decision support systems.

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