Electrocardiogram sequences data analytics and classification using unsupervised and supervised machine learning algorithms Article Swipe

MAKE IMPACT

Sami Ghnimi , Pratapa Raju Moola , J. A. Shariff · YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.11591/ijai.v14.i3.pp2055-2071

This paper explores the prediction of cardiovascular disease (CVD) through the classification of electrocardiogram (ECG) sequences using both supervised and unsupervised machine learning (ML) algorithms. ECG 5000 dataset is considered to perform essential data analytics, clustering, and classification, effectively categorizing ECG heartbeats into optimal groups to forecast CVD. The Elbow and Silhouette methods are applied to estimate optimal number of clusters within the dataset. Using K-means and hierarchical clustering algorithms, the data is grouped into two and five distinguishable clusters, with performance metrics indicating that two clusters are more viable. Subsequently, multiple supervised ML classifiers—including kernel classifiers, support vector machine (SVM), naïve Bayes (NB), decision trees (DT), k-nearest neighbor (KNN) and neural networks (NN)—are trained on the labeled and clustered datasets to ensure accurate classification of ECG sequences and anomaly detection. A novel modified ML classifier, kernel-SVM with Chi-Square (χ²) feature selection, is introduced and demonstrates exceptional performance, achieving an impressive accuracy of 0.9848, recall of 0.9973, and a training time of 1.6944 seconds, surpassing benchmarks from prior research. The results and discussion section includes a comparison of various algorithm performances, affirming that the proposed approach is an alternative to the complex deep learning (DL) and transformer-based models.

Type

article

Language

en

Landing Page

https://doi.org/10.11591/ijai.v14.i3.pp2055-2071

OA Status

diamond

Related Works

10

OpenAlex ID

https://openalex.org/W4411094391

Raw OpenAlex JSON

OpenAlex ID

https://openalex.org/W4411094391

Canonical identifier for this work in OpenAlex

DOI

https://doi.org/10.11591/ijai.v14.i3.pp2055-2071

Digital Object Identifier

Title

Electrocardiogram sequences data analytics and classification using unsupervised and supervised machine learning algorithms

Work title

Type

article

OpenAlex work type

Language

en

Primary language

Publication year

2025

Year of publication

Publication date

2025-06-01

Full publication date if available

Authors

Sami Ghnimi, Pratapa Raju Moola, J. A. Shariff

List of authors in order

Landing page

https://doi.org/10.11591/ijai.v14.i3.pp2055-2071

Publisher landing page

Open access

Yes

Whether a free full text is available

OA status

diamond

Open access status per OpenAlex

OA URL

https://doi.org/10.11591/ijai.v14.i3.pp2055-2071

Direct OA link when available

Concepts

Computer science, Machine learning, Analytics, Artificial intelligence, Unsupervised learning, Data analysis, Learning analytics, Data mining, Algorithm, Pattern recognition (psychology)

Top concepts (fields/topics) attached by OpenAlex

Cited by

0

Total citation count in OpenAlex

Related works (count)

10

Other works algorithmically related by OpenAlex

Full payload