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Increased reliance on electronic health records and plethora of new sensor technologies has enabled the use of machine learning (ML) in medical diagnosis. This has opened up promising opportunities for faster and automated decision making, particularly in early and repetitive diagnostic routines. Nevertheless, there are also increased possibilities of data aberrance arising from environmentally induced noise. It is vital to create ML models that are resilient in the presence of data noise to minimize erroneous classifications that could be crucial. This study uses a recently proposed ML algorithm called the Tsetlin machine (TM) to study the robustness against noise-injected medical data. We test two different feature extraction methods, in conjunction with the TM, to explore how feature engineering can mitigate the impact of noise corruption. Our results show the TM is capable of effective classification even with a signal-to-noise ratio (SNR) of −15dB as its training parameters remain resilient to noise injection. We show that high testing data sensitivity can still be possible at very low SNRs through a balance of feature distribution–based discretization and a rule mining algorithm used as a noise filtering encoding method. Through this method we show how a smaller number of core features can be extracted from a noisy problem space resulting in reduced ML model complexity and memory footprint—in some cases up to 6x fewer training parameters while retaining equal or better performance. In addition, we investigate the cost of noise resilience in terms of energy when compared with recently proposed binarized neural networks.

Resilient Biomedical Systems Design Under Noise Using Logic-Based Machine Learning