Year: 2025
Type: article
Abstract: The prediction of heart disease remains a critical challenge in healthcare, necessitating advanced computational methods to enhance diagnostic accuracy and patient outcomes.This study proposes a hybrid machine learning model integrating Convolutional Neural Networks (CNN) and extreme Gradient Boosting (XG-Boost) to improve heart disease prediction.The CNN component excels in automatically extracting complex features from diverse input data, including medical records, wearable device readings, and genomic information.These extracted features are then fed into the XG-Boost model, known for its robust classification capabilities, to accurately predict the presence or absence of heart disease.
Source: IJARCCE
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4409853351