Open Access
·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667
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The Journal of Information and Optimization Sciences (JIOS) is a world leading journal publishing high quality, rigorously peer-reviewed original research in all mathematically-oriented theoretical and applied topics in information sciences, optimization sciences and related areas since 1980. Subjects include but are not limited to:
• Information Sciences
• Optimization Sciences
• Control Theory
• Operational Research
• Decision Sciences
• Information Theory
• Information Technology
• Computer Networks and Communications
• Mathematical Programming
• Modelling and Simulation
• Database Management
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• Applications to Technology
Issues up to 2022 co-published with and available at:
Improving imbalanced data classification in healthcare systems : A novel optimization approach with statistical insights
*Eshwar DaraCorresponding authordreshwardara@gmail.comMuma College of Business University of South Florida 8350 N. Tamiami Trail Sarasota; Department of Computer Science and Engineering Kommuri Pratap Reddy Institute of Technology (KPRIT)Muma College of Business University of South Florida 8350 N. Tamiami Trail SarasotaFlorida, 33620, U.S.A.View full profile →
, Bhuvan Unhelkarbunhelkar@usf.eduMuma College of Business University of South Florida 8350 N. Tamiami Trail SarasotaFlorida, 33620, USA0000-0003-1118-3837View full profile →
, S. Siva Shankardrsivashankars@gmail.comDepartment of Computer Science and Engineering KG Reddy College of Engineering and TechnologyDepartment of Computer Science and Engineering KG Reddy College of Engineering and TechnologyHyderabad, Telangana, 500100, India0000-0002-7616-6088View full profile →
, Tulika Chakrabartitulika.chakrabarti@spsu.ac.inDepartment of Chemistry Sir Padampat Singhania University Department of Chemistry Sir Padampat Singhania UniversityUdaipur, Rajasthan, 313601, lndiaView full profile →
, Prasun Chakrabartidrprasun.cse@gmail.comDepartment of Computer Science and Engineering Sir Padampat Singhania UniversityUdaipur, Rajasthan, 313601, lndia0000-0001-8062-4144View full profile →
, B. Sivaneasansivaneasan@singaporetech.edu.sgSingapore Institute of Technology Specialist Adult Educator Engineering Singapore Institute of Technology 1 Punggol Coast RoadDepartment of Computer Science and Engineering KG Reddy College of Engineering and Technology Moinabad, Telangana, 500075, IndiaView full profile →
, Martin MargalaMartin.margala@louisiana.eduSchool of Computing and Informatics University of Louisiana at LafayetteLafayette, LA, 70503 (337) 482-6768, 70503 (337) 482-6768View full profile →
* Corresponding author · click or hover a name for details
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.
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