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Cardiovascular diseases (CVD) are the leading cause of death worldwide. Engineered heart tissue produced by differentiation of human induced pluripotent stem cells may provide an encompassing treatment for heart failure due to CVD. However, considerable difficulties exist in producing the large number of cardiomyocytes needed for therapeutic purposes through differentiation protocols. Data-driven modeling with machine learning techniques has the potential to identify factors that significantly affect the outcomes of these differentiation experiments. Using data from previous cardiac differentiation experiments, we have developed data-driven modeling methods for determining which experimental conditions are most influential on the final cardiomyocyte content of a differentiation experiment. With those identified conditions, we were able to build classification models that can predict whether an experiment will have a sufficient cardiomyocyte content to continue with the experiment on the seventh (out of 10) day of the differentiation with a 90% accuracy. This early failure prediction will provide cost and time savings, as each day the differentiation continues requires significant resources.