Cardiovascular Disease Prediction Using Electrocardiogram (ECG) and K-Plus Nearest Neighbors Algorithm: Cases of Chadian Patients


  • KHADIDJA Ousman Kossi National School of ICT, Ndjamena-Chad
  • ALI Ouchar Cherif National Higher Institute of Sciences and Techniques of Abéché, Abéché-Chad
  • MAHAMAT Charfadine Nimane National School of ICT, Ndjamena-Chad
  • ABAKAR Mahamat Ahmat National School of ICT, Ndjamena-Chad


ECG, KNN, artificial intelligence, cardiovascular disease, prediction


This article reviews the use of the electrocardiogram (ECG) and the k-nearest neighbor (KNN) algorithm for the prediction of cardiovascular disease. Cardiovascular diseases are a major public health problem, accounting for a significant proportion of global deaths. The ECG offers a non-invasive method to monitor the electrical activity of the heart, detecting abnormalities and predicting risk. The KNN algorithm, a supervised machine learning technique, is used to classify the examples based on the labeled examples. Using pre-processed ECG data, KNN can recognize characteristic patterns of cardiovascular disease, enabling accurate and rapid prediction. This approach has significant medical potential, enabling early detection and informed decision-making. However, cardiovascular disease prediction is complex and evolving, requiring careful selection of attributes and rigorous evaluation of model performance. The article will include a review of prior designs, choice of study design, performance evaluation criteria, results, and an in-depth discussion of those results.



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How to Cite

Ousman Kossi, K., Ouchar Cherif, A., Charfadine Nimane, M., & Mahamat Ahmat, A. (2023). Cardiovascular Disease Prediction Using Electrocardiogram (ECG) and K-Plus Nearest Neighbors Algorithm: Cases of Chadian Patients. Technium: Romanian Journal of Applied Sciences and Technology, 13, 27–41. Retrieved from