In this study, we use L1 regularization logistic regression as our method. We take fifteen features as input data of model, and we utilize these features to predict (1) which bacteria group the patient belongs to (2) whether the patient is resistant for Amikacin when the bacteria group retain one of these groups, such as Acinetobacter spp., Escherichia coli, Gram negative, Klebsiella spp., Pseudomonas aeruginosa, Staphylococcus spp., and Staphylococcus aureus. Amikacin is an antibiotic medication that are commonly used for several bacterial infections. Therefore, we utilize machine learning to solve this classification problem due to powerful applicable ability of machine learning technique. Predicting which bacteria group the patient belongs to is a multiclass classification while predicting whether the patient is resistant for Amikacin that belongs to binary classification (resistant or susceptibility).
First, for task 1 the experimental results are shown in Table I. We can notice that whatever the penalty weight of L1 is, and the results are similar.