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ANTIBIOTICS RESISTANCE PREDICTION USING MACHINE LEARNING ALGORITHMS

Authors: Bharathi A , Gokul A , Naveen Kumar T , Ranjith V

ABSTRACT

Opioid Risk Antibiotics Resistance abuse, overdose, and drug addiction have become public health concerns as a result of the notable increase in the use of both prescribed and over-the-counter medications in the United States. The growing incidence of opioid usage presents a complex public health issue, one of which is the possible influence on antibiotic resistance. The existing system predicts antibiotic resistance profiles in bacterial infections among hospitalized patients using machine learning (ML) algorithms and electronic medical records (EMRs). However, it ignores the impact of opioid use, a major public health issue that may be connected to antibiotic resistance. The complex association between opioid-related behaviors and antibiotic resistance may not be adequately captured by powerful machine learning algorithms, despite their use. The larger context of antibiotic resistance patterns is limited by the lack of linkage with data on opioid intake. The proposed abstract aims to improve predictive accuracy and understanding of the complex factors influencing antibiotic resistance emergence by integrating support vector machines (SVM) and linear regression to thoroughly analyse the relationship between opioid risk and antibiotic resistance. This approach addresses these limitations. We suggested combining linear regression and support vector machines (SVM) to better understand the complex connection between opioid risk and antibiotic resistance. In order to pinpoint the main causes of increased risk, trends in opioid consumption data are analyzed using the SVM model. In parallel, the relationship between opioid abuse and the rise in antibiotic resistance is examined using linear regression. Our goal is to have a thorough grasp of the complex relationship between opioid-related behaviors and the emergence of antibiotic resistance by combining these two analytical approaches. This project is designed under NetBeans with java as front end. Weka tool for machine learning analysis

Keywords: Antibiotic Resistance, Machine Learning, Bacterial Infections, Global Crisis, Healthcare.