Authors:
Oluebube F. Odoemena, Gabriel A. Elufidodo

Addresses:
1,2, Department of Computer Science, University of Nigeria, Nsukka, Nigeria. oluebube.ikwuagwu.pg91407@unn.edu.ng1, gabriel.elufidodo@unn.edu.ng2 

Abstract:

Cardiovascular diseases (CVDs) are a major cause of death worldwide, emphasizing the importance of better early detection methods. This study introduces a machine learning model to detect CVDs by analyzing vital sign information. The model, which was trained using a cardiovascular dataset, utilizes three algorithms: Support Vector Machine (SVM), Naïve Bayes, and Decision Tree. It assesses the health status of patients by predicting vital sign values, allowing healthcare providers to receive timely alerts. Following the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology, a proof-of-concept web application was created with object-oriented design principles and implemented using Python. This application predicts the likelihood of CVD and streamlines the scheduling of doctor appointments, promoting quick medical intervention. Our findings reveal that the Decision Tree classifier performed the best in accurately identifying patients who are at risk based on abnormalities in vital signs. This method can potentially enhance early detection of CVDs and improve the timing of medical care.

Keywords: Cardiovascular Diseases; Machine Learning; Early Detection; Vital Signs; Support Vector Machine; Naive Bayes; Decision Tree; Cross-Industry Standard Process for Data Mining (CRISP-DM); Web Application; Healthcare Automation.

Received on: 11/01/2024, Revised on: 06/03/2024, Accepted on: 04/04/2024, Published on: 01/06/2024

DOI: 10.69888/FTSHSL.2024.000174

FMDB Transactions on Sustainable Health Science Letters, 2024 Vol. 2 No. 2, Pages: 81-93

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