Determination of Antibiotic Resistance Level in Klebsiella using Machine Learning Models

Antimicrobial drug resistance (AMR) in bacteria is a public health hazard and is growing alarmingly. There is a development of multidrug-resistant organisms due to the selective pressure exerted on organisms by drugs. Due to delay in antibiotic susceptibility testing results, artificial intelligence (AI) is employed to control the organism’s resistance against the last resort drugs and speeding up the AMR detection process. Therefore, machine learning (ML), a mathematical tool for AI, is used. For this study, 6 classification ML models were used to train and forecast the resistance of β-lactam drugs in Klebsiella pneumoniae and were carried out on orange tool. Out of the 6 ML classifier models, KNN and random forest outperformed the remaining 4 classifiers. The purpose of this research was to develop an AI-based model to classify strains based on specific features.