Background Machine learning based on clinical characteristics has the potential to predict coronary CT angiography (CCTA) findings and help guide resource utilisation.Methods From the SCOT-HEART ...

🩺 Haar_ECG: ECG Signal Denoising and Arrhythmia Detection Using Haar Wavelet + Deep Learning This project applies Haar wavelet transform for ECG signal denoising and trains CNN/SVM classifiers to ...

For example, instead of using raw ECG data as a covariate in a disease prediction ML problem, the researcher may only use the QRS duration. More recently, feature learning has taken the role of ...

Our optimal feature selection-based ML model accurately and reliably predicts CT abnormalities in mTBI patients using routine test data. By addressing clinicians' concerns regarding transparency and ...

This study aims to harness machine learning to identify the most effective miRNA biomarkers for breast cancer detection. By using feature selection and random forest algorithms, we sought to identify ...

Developing a novel method to detect the disease early improves the quality and efficiency of medical care. Methods: The paper presents various modern approaches for classifying cardiac diseases from ...

We explore different methods of feature selection needed to improve the performance of a machine learning model in the detection of the onset of AD. We present different techniques used as well as the ...

Conclusions: An ECG-based machine learning model using a composite end point can identify a high-risk population for having undiagnosed, clinically significant structural heart disease while ...