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DnnARs: An Artificial Intelligence Technique for Prediction of Antimicrobial Resistant Strains in E. coli Bacteria Causing Urine Tract Infection

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Abstract

The rise of antimicrobial resistance (AMR) in bacteria that cause infectious diseases poses a significant global health challenge, necessitating advanced prediction technology for effective management and response. This study aims to evaluate the predictive capabilities of several machine learning (ML) and deep learning (DL) models in identifying patterns of antibiotic resistance. We have proposed a comprehensive experimental framework that combines multiple machine learning algorithms and deep learning architectures to evaluate their efficacy and accuracy in predicting antibiotic resistance. We have developed DnnARs, an artificial intelligence pipeline designed to identify antibiotic-resistant strains of E. coli bacteria, which are mostly responsible for urinary tract infections (UTIs). DnnARs utilizes Synthetic Minority Over-sampling Technique (SMOTE) and applies feature engineering using principal component analysis (PCA) and t-distributed Stochastic neighbor embedding (t-SNE) on our dataset to evaluate the effectiveness of various AI models. Our model comparison demonstrates that DL models outperform ML models when the dataset is fine-tuned using SMOTE and feature engineering. Our DnnARs achieve a classification accuracy of 97%, a specificity of 91%, and a remarkable sensitivity of 100%. The DL models that were examined exhibit an average classification accuracy that is 3.66% higher than that of the ML models. The findings demonstrate the capability of deep learning methodology to enhance predictive analytics for antimicrobial resistance, offering a robust and efficient alternative to traditional machine learning techniques. This research aims to enhance diagnostic tools and medications, improve the management of infectious diseases, reduce antibiotic resistance, and integrate deep learning models into clinical workflows to enable immediate decision-making.

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Data Availability

The datasets utilized for this study are freely available and can be found at NCBI with accession numbers GSE98505 and GSE96706.

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Funding

The authors have received no external funding for this work.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, FET-ITER, Siksha ‘O’ Anusandhan (Deemed to Be) University, Bhubaneswar, India

    Debasish Swapnesh Kumar Nayak

  2. Department of Computer Science and Engineering, SOET, Centurion University of Technology and Management, Bhubaneswar, Odisha, India

    Debasish Swapnesh Kumar Nayak

  3. Department of Statistics, Utkal University, Bhubaneswar, India

    Arpita Priyadarshini, Pabani Mahanta & Tejaswini Das

  4. Department of Electronics and Communication Engineering, FET-ITER, Siksha ‘O’ Anusandhan (Deemed to Be) University, Bhubaneswar, India

    Santanu Kumar Sahoo

  5. Department of Computer Application, National Institute of Technology, Raipur, India

    Tripti Swarnkar

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  1. Debasish Swapnesh Kumar Nayak
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Contributions

DSKN, SS, and TS designed the model and the computational framework and analyzed the data. DSKN carried out the simulations and implementation. DSKN, AP, PM, and TD performed the calculations. DSKN wrote the manuscript with input from all authors. DSKN, SS, and TS conceived the study and were in charge of overall direction and planning.

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Correspondence to Debasish Swapnesh Kumar Nayak.

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Nayak, D.S.K., Priyadarshini, A., Mahanta, P. et al. DnnARs: An Artificial Intelligence Technique for Prediction of Antimicrobial Resistant Strains in E. coli Bacteria Causing Urine Tract Infection. SN COMPUT. SCI. 5, 1091 (2024). https://doi.org/10.1007/s42979-024-03452-6

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