We applied the algorithms above to two distinct medical datasets, the mass spectrometry data of Staphylococcus aureus for predicting methicillin-resistance (“Mass spectrometry” dataset: 3338 cases 268 features), and the urinalysis data for predicting Trichomonas vaginalis infection (“Urinalysis” dataset: 839,164 cases 9 features). To explore and compare the energy efficiencies of widely-used machine learning (ML) algorithms, including logistic regression (LR), k-nearest neighbors (kNN), support vector machine (SVM), random forest (RF), extreme gradient boosting (XGB), and two different neural networks (NN) in the medical datasets. However, the energy efficiencies of different AI models used for medical applications have not yet been studied. Numerous energy optimization techniques have been developed to relieve the burden on the hardware required to deploy a complex learning model. Different from other type of data in visual AI, data in medical domain are usually composed of features with strong signals. An AI model must be energy-efficient if it has to be used for inference applications in medical domain. Harnessing artificial intelligence (AI) in medical domain has raised considerable interest recently.
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