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A study on heart data analysis and prediction using advanced machine learning methods    
Yazarlar (1)
Dr. Öğr. Üyesi Serbun Ufuk DEĞER Dr. Öğr. Üyesi Serbun Ufuk DEĞER
Kastamonu Üniversitesi, Türkiye
Devamını Göster
Özet
Cardiovascular diseases comprise a diverse array of disorders impacting the cardiac structure and vascular system and rank among the predominant factors contributing to mortality on a global scale. Every day, a significant number of individuals die from various heart-related issues. Therefore, early detection of heart diseases is of critical importance. Especially following these diagnoses, providing a more accurate diagnosis for individuals at high risk and subsequent extra treatments outline an essential roadmap for preventing heart attacks. This paper compares the performance of classic machine learning (ClassicML) and automated machine learning (AutoML) models across different variations that incorporate feature engineering and balancing techniques, thereby identifying which machine learning model is more successful in the development and implementation of patient-centered systems for the early ...
Anahtar Kelimeler
AutoML | ClassicML | Heart disease prediction | Machine learning | Partial derivative | Visualization
Makale Türü Özgün Makale
Makale Alt Türü Uluslararası alan indekslerindeki dergilerde yayımlanan tam makale
Dergi Adı Computers in Biology and Medicine
Dergi ISSN 0010-4825 Wos Dergi Scopus Dergi
Dergi Tarandığı Indeksler
Makale Dili İngilizce
Basım Tarihi 06-2025
Cilt No 192
Sayı 1
Sayfalar 110308 / 0
Doi Numarası 10.1016/j.compbiomed.2025.110308
BM Sürdürülebilir Kalkınma Amaçları
Atıf Sayıları
A study on heart data analysis and prediction using advanced machine learning methods

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