TY - JOUR
AU - A. Kahraman
A1 - 
AB - Cardiovascular disease continues to cause an important global health challenge, highlighting the critical importance of early detection in mitigating cardiac-related issues. There is a significant demand for reliable diagnostic alternatives. Taking advantage of health data through diverse machine learning algorithms may offer a more precise diagnostic approach. Machine learning-based decision support systems that utilize patients' clinical parameters present a promising solution for diagnosing cardiovascular disease. In this research, we collected extensive publicly available healthcare records. We integrated medical datasets based on common features to implement several machine learning models aimed at exploring the potential for more robust predictions of cardiovascular disease (CVD). The merged dataset initially contained 323,680 samples sourced from multiple databases. Following data preprocessing steps including cleaning, alignment of features, and removal of missing values, the final dataset consisted of 311,710 samples used for model training and evaluation. In our experiments, the CatBoost model achieved the highest area under the curve (AUC) of up to 94.1%.
AD - Department of Computer Engineering, Faculty of Engineering, Balıkesir University, Balıkesir, Türkiye.
AN - 41446898
BT - Front Artif Intell
C5 - HIT & Telehealth
DO - 10.3389/frai.2025.1694450
DP - NLM
ET - 20251209
JF - Front Artif Intell
LA - eng
N2 - Cardiovascular disease continues to cause an important global health challenge, highlighting the critical importance of early detection in mitigating cardiac-related issues. There is a significant demand for reliable diagnostic alternatives. Taking advantage of health data through diverse machine learning algorithms may offer a more precise diagnostic approach. Machine learning-based decision support systems that utilize patients' clinical parameters present a promising solution for diagnosing cardiovascular disease. In this research, we collected extensive publicly available healthcare records. We integrated medical datasets based on common features to implement several machine learning models aimed at exploring the potential for more robust predictions of cardiovascular disease (CVD). The merged dataset initially contained 323,680 samples sourced from multiple databases. Following data preprocessing steps including cleaning, alignment of features, and removal of missing values, the final dataset consisted of 311,710 samples used for model training and evaluation. In our experiments, the CatBoost model achieved the highest area under the curve (AUC) of up to 94.1%.
PY - 2025
SN - 2624-8212
SP - 1694450
ST - Machine learning techniques for improved prediction of cardiovascular diseases using integrated healthcare data
T1 - Machine learning techniques for improved prediction of cardiovascular diseases using integrated healthcare data
T2 - Front Artif Intell
TI - Machine learning techniques for improved prediction of cardiovascular diseases using integrated healthcare data
U1 - HIT & Telehealth
U3 - 10.3389/frai.2025.1694450
VL - 8
VO - 2624-8212
Y1 - 2025
ER -