One approach to improve CHF detection with machine learning is to sidestep the use of expensive and time-consuming imaging studies. A recent study from Keimyung University in South Korea used rough sets, decision trees, and logistic regression algorithms, and compared their performance to a physician's diagnosis of heart disease, which was used as the gold standard (Son et al., 2012).

Rough sets are similar to best subset logistic regression (for example, subsets of variables are tested for their informativeness, and the most informative subsets are chosen for the final decision rules). The algorithms were trained on demographic characteristics and lab findings only. The models achieved over 97% sensitivity and 97% specificity in differentiating CHF from non-CHF-related shortness of breath. That's astonishingly close to the human performance, with much less data, time, and resources used.

A second approach we should mention is the use of automated algorithms to read the echocardiography and cardiac MRI scans used to diagnose CHF. This problem was the subject of the 2015 Data Science Bowl, sponsored by the data science competition website Kaggle and Booz Allen Hamilton, the consulting company. For more on this competition, you can visit the Kaggle competition website: https://www.kaggle.com/c/second-annual-data-science-bowl. It goes to show the interest that machine learning in healthcare has garnered.