AntiMicro.ai: An Artificial Intelligence powered web application for predicting antibacterial/antifungal susceptibility and constructing personalized machine learning models

Background To train and develop machine learning models on the Pfizer antibacterial and antifungal datasets with subsequent deployment to an interactive Web Application. Methods We utilized R version 4.3.1 to perform descriptive analysis to obtain features/predictors. Python 3.10 libraries NumPy, Pandas, Scikit learn, Pycaret were used to train machine learning models. All these models were scored using area under the curve, recall, precision, F1, Kappa and the Mathews correlation coefficient. The best performing model was then deployed into a web application built on Streamlit after which deployment was done using GitHub and Streamlit cloud. A prototype android app was also developed using GoNative. Results The exploratory data analysis, S Aureus (17.2%) was the most common species however in the sub group analysis of the isolates with genotypic values, K Pneumoniae(48.2%) and E Coli (36.9%) were dominant. Amongst the fungi, Candida albicans (38.3%) and Candida glabrata (15.5%) were dominant. Feature selection was done using Shapley additive explanation plots. Using Extreme Gradient Boosting (XGBoost), we were able to achieve 99% and 97.8% AUC in the prediction of antibacterial and antifungal susceptibility respectively with minimal overfitting. Conclusions Decision tree based methods are a viable options of predicting antibacterial and antifungal drug resistance. When presented in visually appealing modes like web applications and android apps, it can be a useful guide to clinicians on initial treatment while awaiting definitive phenotypic testing. It can also be a surveillance tool that can craft antimicrobial resistance strategies.