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Due to the limitations of current NISQ systems, error mitigation strategies are under development to alleviate the negative effects of error-inducing noise on quantum applications. This work proposes the use of machine learning (ML) as an error mitigation strategy, using ML to identify the accurate solutions to a quantum application in the presence of noise. Methods of encoding the probabilistic solution space of a basis-encoded quantum algorithm are researched to identify the characteristics which represent good ML training inputs. A multilayer perceptron artificial neural network (MLP ANN) was trained on the results of 8-state and 16-state basis-encoded quantum applications both in the presence of noise and in noise-free simulation. It is demonstrated using simulated quantum hardware and probabilistic noise models that a sufficiently trained model may identify accurate solutions to a quantum applications with over 90% precision and 80% recall on select data. The model makes confident predictions even with enough noise that the solutions cannot be determined by direct observation, and when it cannot, it can identify the inconclusive experiments as candidates for other error mitigation techniques.