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Cited by 23Artificial Intelligence in HealthcareMachine Learning and ELMImbalanced Data Classification Techniques

Edge Computing-Based Ensemble Learning Model for Health Care Decision Systems

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Vincent, Asir Chandra Shinoo Robert, Sengan, Sudhakar·2024·Scientific Reports
DOI 10.1038/s41598-024-78225-5PMC PMC11541999
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Abstract

A growing number of humans have suffered severe chronic illnesses, which has caused a boost in the requirement for diagnostic and medical treatment procedures that are both accurate and fast. Improved patient conditions and enhanced Decision-Making Systems (DMS) for healthcare professionals are the primary objectives of the Clinical Decision Support System (CDSS) recommended in this research article. The main drawback of traditional Machine Learning (ML) techniques is their failure to predict reliably. To solve this problem, the proposed model creates an Ensemble Extreme Learning Machine (EN-ELM) algorithm that combines predictors trained on several different data sets. This lowers the chance of overfitting. The suggested CDSS uses many different data processing methods, including Adaptive Synthetic (ADASYN) and isolation Forest (iForest), which fix problems like outliers and class imbalance. This approach significantly enhances the framework's classification performance. Also, the CDSS is compatible with an EC model, which enables real-time computation while minimizing the requirement for integrated systems. The recommended CDSS applies iForest and ADASYN to execute large-scale trials validating high standards of accuracy across numerous datasets. Researchers concluded that a suitable ELM classification threshold of 85% is the most effective, which substantially boosts the accuracy of the predictive model. When applied to various medical datasets, such as Hepatocellular Carcinoma (HCC), Cervical Cancer, Chronic Kidney Disease (CKD), Heart Disease, and Arrhythmia, the EN-ELM achieved accuracy rates of 99.36%, 98.15%, 97.85%, 97.06%, and 96.72%, respectively. By measuring this progress, the CDSS could dramatically improve the accuracy of chronic illness diagnosis and treatment, which similarly affects clinicians.

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  • Some CDSS can track disease progression, predict outcomes, and support follow-up planning.

    “Additionally, some CDSS can track disease progression, predict outcomes, and support follow-up planning [224,225].”

    From Toward Safer Diagnoses: A SEIPS-Based Narrative Review of Diagnostic Errors (2026) · cites this paper as [224]

    The Proposed CDSS architecture describes real-time monitoring and storing patient data for analysis over time; the Dataset description identifies one-year survival as a prediction target. The Introduction says CDSS can recommend further treatment, but our copy does not establish that they support follow-up planning specifically. No page numbers are available in our copy. (LLM, conf=0.84)

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Cited by

Sorted by citations

Authors
Vincent, A. C. S. R., & Sengan, S.
Corresponding author
  • Vincent, Asir Chandra Shinoo Robert
  • Sengan, Sudhakar
ORCID
Sengan, Sudhakar
Venue
Scientific Reports
Publisher
Springer Science and Business Media LLC
Published
2024-11-06
Volume
14
Issue
1
Pages
26997-26997
ISSN
2045-2322 (ISSN-L)
DOI
10.1038/s41598-024-78225-5
PMC
PMC11541999
OpenAlex
W4404106811
Cited by
23
References
1
Citations by year
2026: 7 · 2025: 15 · 2024: 1
FWCI
7.03
Type
journal article
Language
en
Open access
Licence
Locations
  • Scientific Reports (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Topics
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and ELM
  • Physical Sciences › Computer Science › Artificial Intelligence › Imbalanced Data Classification Techniques
Keywords
edge computing, clinical decision support systems, chronic disease diagnosis, ADASYN, Isolation Forest, class imbalance, outlier handling, hepatocellular carcinoma, cervical cancer, chronic kidney disease, heart disease, cardiac arrhythmias
MeSH terms
Machine Learning, Algorithms, Humans, Decision Support Systems, Clinical

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