A novel software engineering approach toward using machine learning for improving the efficiency of health systems
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Abstract
Abstract Background We propose a novel framework for health Informatics: framework and methodology of Software Engineering for machine learning in Health Informatics (SEMLHI). This framework shed light on its features, that allow users to study and analyze the requirements, determine the function of objects related to the system and determine the machine learning algorithms that will be used for the dataset.Methods Based on original data that collected from the hospital in Palestine government in the past three years, first the data validated and all outlier removed, analyzed using develop framework in order to compare ML provide patients with real-time. Our proposed module comparison with three Systems Engineering Methods Vee, agile and SEMLHI. The result used by implement prototype system, which require machine learning algorithm, after development phase, questionnaire deliver to developer to indicate the result using three methodology. SEMLHI framework, is composed into four components: software, machine learning model, machine learning algorithms, and health informatics data, Machine learning Algorithm component used five algorithms use to evaluate the accuracy for machine learning models on component.Results we compare our approach with the previously published systems in terms of performance to evaluate the accuracy for machine learning models, the results of accuracy with different algorithms applied for 750 case, linear SVG have about 0.57 value compared with KNeighbors classifier, logistic regression, multinomial NB, random forest classifier. This research investigates the interaction between SE, and ML within the context of health informatics, our proposed framework define the methodology for developers to analyzing and developing software for the health informatic model, and create a space, in which software engineering, and ML experts could work on the ML model lifecycle, on the disease level and the subtype level.Conclusions This article is an ongoing effort towards defining and translating an existing research pipeline into four integrated modules, as framework system using the dataset from healthcare to reduce cost estimation by using a new suggested methodology. The framework is available as open source software, licensed under GNU General Public License Version 3 to encourage others to contribute to the future development of the SEMLHI framework.
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Moreb et al. proposed a process for engineering ML systems in health informatics domain.
“Moreb et al. (2020) proposed a process for engineering ML systems in health informatics domain.”
From A software engineering perspective on engineering machine learning systems: State of the art and challenges (2021) · cites this paper as Moreb et al., 2020
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Cited by
Sorted by citations
Giray, G. (2021) · Journal of Systems and Software · doi:10.1016/j.jss.2021.111031 · Cited by 187 · see citing works
- Authors
- Moreb, M., Mohammed, T. A., & Bayat, O.
- Affiliations
- Altınbaş University (TR)
- University of Kirkuk (IQ)
- Venue
- IEEE Access
- Published
- 2020
- Volume
- 8
- Pages
- 23169-23178
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W3003566556
- Cited by
- 39
- References
- 44
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 7 · 2023: 9 · 2022: 3 · 2021: 15 · 2020: 2
- FWCI
- 6.82
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion, cc-by)
- Research Square (acceptedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Information Systems › Software Engineering Techniques and Practices
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- software engineering framework, health system efficiency, user needs analysis, machine learning algorithm selection, health data privacy and security, hospital data
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