Prediction of Antimicrobial Resistance for Disease-Causing Agents Using Machine Learning
Srajan Kulshrestha, Sanjana Panda, Dinesh Nayar, Vaishali Dohe, Ashwini Jarali · 2018
Antimicrobial resistance (AMR) occurs when disease-causing microorganisms are resistant towards prescribed drugs, nullifying its effect. As a consequence, there is a delay in recovery which worsens the patient's health. Antimicrobial resistance is identified as a global threat by the medical fraternity and various government bodies. Objective of the proposed system is to integrate technology with the field of bio-medical, in context with AMR. We applied various machine learning algorithms on datasets, to identify patterns and use them to predict resistance towards various drugs. This model would help in closing the gap between Doctors and Labs. In this model, we used ML and data mining techniques to predict AMR for individual patients based on trends identified from datasets. For building the model we use results of Patients undergoing antibiotic susceptibility test as datasets.