Data analytics for cardiac diseases. (March 2022) - Juhola, Martti; Joutsijoki, Henry; Penttinen, Kirsi; Shah, Disheet; Pölönen, Risto-Pekka; Aalto-Setälä, Katriina
Abstract: 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. Highlights: Data analysis was performed for calcium transient signals measured from induced pluripotent stem cell-derived cardiomyocytes. Machine learning tests were computed with an extensive set of methods. Classification was performed for the data of seven disease classes and controls. Random forests gave the best classification results.