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Journal of Information and Optimization Sciences cover

Journal of Information and Optimization Sciences

(Since 1980)
Hybrid ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

WoS  JIF 2026 : 0.4 (Q4)

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Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

Issues up to 2022 co-published with and available at:Taylor & Francis
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Journal of Information and Optimization Sciences
Open Access Research Article

Improving imbalanced data classification in healthcare systems : A novel optimization approach with statistical insights

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pp. 2203–2212Vol. 45Issue 8November 2024DOI: 10.47974/JIOS-1783XML
Published Online:
18 Dec 2024
Article type:
Research Article
Language:
EN
Article no.:
JIOS-1783
Pages:
2203–2212

Abstract

Imbalanced data classification poses a challenge in medical intelligent diagnosis due to limited samples and high-dimensional features in real-world biomedical datasets. This issue impacts the classification accuracy and can lead to erroneous disease diagnoses. Addressing this challenge requires effective methods tailored for imbalanced and limited biomedical datasets. In this study, we introduce a novel approach, the Artificial Jellyfish Optimization Based Advanced Dynamic Neural Fuzzy (AJO-ADNF) classification model, coupled with a Hybrid Shuffle Shepherd based Sampling Method (HSS-SM). Initially, irrelevant features are removed through preprocessing, while significant pathogenic traits are identified. The HSS-SM algorithm balances the dataset by providing reasonable minority and majority class data, mitigating computational burden. Subsequently, the AJO-ADNF classifier is applied to classify the balanced dataset. This approach generates genuine minority and majority class data and autonomously selects optimal learning model parameters, enhancing robustness and efficiency. Evaluation on COVID-19 healthcare datasets using MATLAB demonstrates superior classification performance compared to conventional methods in terms of Accuracy, Sensitivity, Specificity, AUC, F-measure, and other metrics.

Keywords

Imbalanced dataMachine learningStatistical insightsOptimizationNeural fuzzySamplingPerformance measuresHealthcare and big data

Subject Classifications

68T0792C55

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