Health-Related Data Analysis Using Metaheuristic Optimization and Machine Learning

Darmawahyuni, Annisa; Nurmaini, Siti; Tutuko, Bambang; Rachmatullah, Muhammad Naufal; Firdaus, Firdaus; Sapitri, Ade Iriani; Islami, Anggun; Marcelino, Jordan; Isdwanta, Rendy; Karim, Muhammad Irfan

doi:10.1109/access.2024.3390008

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Other2

Article1

Relationship

Self Cite0

Independent3

Authors

Journals

Cited by 3 publications

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Optimizing Chronic Kidney Disease Diagnosis: A Comprehensive Review of Metaheuristic Algorithms and Their Applications in Detection and Classification

Manjula,

Hema Rajini,

Chokkanathan

2024

2024 8th International Conference on Electronics, Communication and Aerospace Technology (ICECA)

View full text Add to dashboard Cite

Optimizing Chronic Kidney Disease Diagnosis: A Comprehensive Review of Metaheuristic Algorithms and Their Applications in Detection and Classification

Manjula,

Hema Rajini,

Chokkanathan

2024

2024 8th International Conference on Electronics, Communication and Aerospace Technology (ICECA)

View full text Add to dashboard Cite

Experimented Multi-Objective Teaching-Learning-Based Optimization for Medical Data Analysis

Darmawahyuni,

Nurmaini,

Tutuko

et al. 2024

2024 International Conference on Data Science and Its Applications (ICoDSA)

View full text Add to dashboard Cite

An improved electrocardiogram arrhythmia classification performance with feature optimization

Darmawahyuni,

Nurmaini,

Tutuko

et al. 2024

BMC Med Inform Decis Mak

View full text Add to dashboard Cite

Background Automatic classification of arrhythmias based on electrocardiography (ECG) data faces several significant challenges, particularly due to the substantial volume of clinical data involved in ECG signal analysis. The volume of clinical data has increased considerably, especially with the emergence of new clinical symptoms and signs in various arrhythmia conditions. These symptoms and signs, which serve as distinguishing features, can number in the tens of thousands. However, the inclusion of irrelevant features can lead to inaccurate classification results. Method To identify the most relevant and optimal features for ECG arrhythmia classification, common feature extraction techniques have been applied to ECG signals, specifically shallow and deep feature extraction. Additionally, a feature selection technique based on a metaheuristic optimization algorithm is utilized following the ECG feature extraction process. Results Our findings indicate that shallow feature extraction based on the time-domain analysis, combined with feature selection using a metaheuristic optimization algorithm, outperformed other ECG feature extraction and selection techniques. Among eight features of time-domain anaylsis, the selected feature is one to three features from RR-interval assesment, achieving 100% accuracy, sensitivity, specificity, and precision for ECG arrhythmia classification. Conclusion The proposed end-to-end architecture for ECG arrhythmia classification demonstrates simplicity in parameters and low complexity, making it highly effective for practical applications.

show abstract

Health-Related Data Analysis Using Metaheuristic Optimization and Machine Learning

Abstract: We thank Intelligent System Research Group (ISysRG) Universitas Sriwijaya, Indonesia for its full support of our research infrastructure.

Cited by 3 publications

References 49 publications

Optimizing Chronic Kidney Disease Diagnosis: A Comprehensive Review of Metaheuristic Algorithms and Their Applications in Detection and Classification

Optimizing Chronic Kidney Disease Diagnosis: A Comprehensive Review of Metaheuristic Algorithms and Their Applications in Detection and Classification

Experimented Multi-Objective Teaching-Learning-Based Optimization for Medical Data Analysis

An improved electrocardiogram arrhythmia classification performance with feature optimization

Contact Info

Product

Resources

About