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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
Advanced machine learning techniques for cardiovascular disease early detection and diagnosis
Journal of Big Data, Volume 10, No. 1, Article 144, Year 2023
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Description
The identification and prognosis of the potential for developing Cardiovascular Diseases (CVD) in healthy individuals is a vital aspect of disease management. Accessing the comprehensive health data on CVD currently available within hospital databases holds significant potential for the early detection and diagnosis of CVD, thereby positively impacting disease outcomes. Therefore, the incorporation of machine learning methods holds significant promise in the advancement of clinical practice for the management of Cardiovascular Diseases (CVDs). By providing a means to develop evidence-based clinical guidelines and management algorithms, these techniques can eliminate the need for costly and extensive clinical and laboratory investigations, reducing the associated financial burden on patients and the healthcare system. In order to optimize early prediction and intervention for CVDs, this study proposes the development of novel, robust, effective, and efficient machine learning algorithms, specifically designed for the automatic selection of key features and the detection of early-stage heart disease. The proposed Catboost model yields an F1-score of about 92.3% and an average accuracy of 90.94%. Therefore, Compared to many other existing state-of-art approaches, it successfully achieved and maximized classification performance with higher percentages of accuracy and precision. © 2023, Springer Nature Switzerland AG.
Authors & Co-Authors
Baghdadi, Nadiah Abdulaziz
Saudi Arabia, Riyadh
Princess Nourah Bint Abdulrahman University
Farghaly Abdelaliem, Sally Mohammed
Saudi Arabia, Riyadh
Princess Nourah Bint Abdulrahman University
Malki, Amer S.
Saudi Arabia, Madinah
Taibah University
Gad, Ibrahim
Egypt, Tanta
Tanta University
Ewis, Ashraf Abd Elazeem
Egypt, Minya
Minia University
Saudi Arabia, Meccah
Umm Alqura University
Atlam, El Sayed
Saudi Arabia, Madinah
Taibah University
Egypt, Tanta
Tanta University
Statistics
Citations: 8
Authors: 6
Affiliations: 5
Identifiers
Doi:
10.1186/s40537-023-00817-1
ISSN:
21961115
Research Areas
Health System And Policy
Noncommunicable Diseases
Study Design
Randomised Control Trial