Machine learning algorithms, particularly random forests, show potential for differentiating genetic cardiac diseases using calcium transient signals from iPSC-derived cardiomyocytes.
Machine learning may aid genetic cardiac disease differentiation from iPSC signals; hypothesis-generating and requires human validation before any clinical consideration.
In the present research we tackled the classification of seven genetic cardiac diseases and control subjects by using an extensive set of machine learning algorithms with their variations from simple K-nearest neighbor searching method to support vector machines. The research was based on calcium transient signals measured from induced pluripotent stem cell-derived cardiomyocytes. All in all, 55 different machine learning alternatives were used to model eight classes by applying the principle of 10-fold crossvalidation with the peak data of 1626 signals. The best classification accuracy of approximately 69% was given by random forests, which can be seen high enough here to show machine learning to be potential for the differentiation of the eight disease classes.
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Juhola et al. (2022) studied this question.
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