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Can human expertise improve the diagnosis of CAD?

The results of this study demonstrate the potential for this approach to improve the diagnosis of CAD and highlight the importance of considering the role of human expertise in the development of computer-aided classification models.

How does a prediction method work?

The method tries to split the goal among all the features in a fair manner or, alternatively, assign each feature an importance value for a particular prediction, depending on how much each feature contributed to the final score/goal.

What are the risk factors for CAD?

These are very well known indicating risk-factors when studying a patient for CAD 45, 46, 47, 48, 49, 50, 51, 52. However, an unexpected result was that other extremely common CAD risk-factors were absent. In particular, smoking was only included in 2 subsets, whereas Dyslipidemia was included in only two, as well.

Should ML algorithms be used to predict CAD instances?

Ideally, for healthy subjects, this procedure should be avoided. There is a plethora of related work 4, 5, 6, 7, 8, 9, 10 where common ML algorithms are used to predict CAD instances with varying results, ranging accuracy-wise from 71.1% to over 98% when also employing image data.

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