What is ML-based CAD diagnosis?

ML-based CAD diagnosis ML-based CAD detection is a pure machine learning problem. While the model simplicity, interpretability, and computational burden are important factors, the doctors and practitioners are mainly concerned about the reliability and overall performance of the model in the detection of CAD.

What does CAD stand for?

Anyone you share the following link with will be able to read this content: The main goal driving this work is to develop computer-aided classification models relying on clinical data to identify coronary artery disease (CAD) instances with high accuracy while incorporating the expert’s opinion as input, making it a "man-in-the-loop" approach.

Can machine learning classification models be used to diagnose CAD?

This work aims to introduce a new tool based on Machine Learning (ML) classification models meant to be used in a consultative manner by experts in the process of diagnosis. Single Photon Emission Computed Tomography (SPECT) and Positron Emission Tomography (PET) are the main tools used for the visual assessment of CAD.

Which ML algorithms are used for CAD detection?

Additionally, the results demonstrated that ANNs, DTs, SVMs, Naïve Bayes, and KNN are the most widely used algorithms for CAD detection. Due to inherent differences among datasets, inconsistent performances have been reported for different datasets using similar ML algorithms.