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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.

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Index Terms

Computer Science
Information Sciences

Keywords

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

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