> quick back of the envelope calculation a positive test result is still 5× more likely to be a false positive than a true positive.
For what it's worth, about 10% of people who come in to the cardiology clinic experiencing symptoms are diagnosed with an abnormal heart rhythm. So even a 20% positive predictive value would be an improvement over the status quo.
As mentioned below, you can use other risk factors (like CHA2DS2-Vasc, or even simply age) to raise the pre-test probability, and thereby control the false positive rate.
As a meta-point, I do think we let the perfect be the enemy of the good in medicine, and that potentially scares people away who could otherwise make positive contributions. For example, many of the most common screening methods in use today are simple, linear models with c-statistics below 0.8. You can build a far-from-perfect system, and still improve dramatically over how people receive healthcare today.
My overall message to machine learning practitioners sitting on the sidelines would be: please join our field. The status quo in medicine is much more primitive than we have been led to believe, and your skills can very literally save lives.