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This study aims to enhance society by detecting early antimicrobial resistance in Candida species and improving healthcare outcomes. They involve the use of AMRSN data, a Python environment with required libraries, and machine learning models to predict resistance. The main goal is to detect their possible resistance in time to help doctors make better decisions and treatment choices. It highlights two medicines: Anidulafungin reliable against Candida and Caspofungin. Additionally, a predictive model such as K-nearest neighbors (KNNs) is used to identify trends in drug resistance, which is useful in planning strategies for treatment.

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Revolutionizing Antifungal Treatment: Machine Learning Insights into Candida Species Antimicrobial Resistance Patterns for Informed Clinical Decisions

  • 会议论文

  • 发表日期：

    2025年

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  • Shravya,
  • Manish,
  • M. Ramakrishna

摘要

This study aims to enhance society by detecting early antimicrobial resistance in Candida species and improving healthcare outcomes. They involve the use of AMRSN data, a Python environment with required libraries, and machine learning models to predict resistance. The main goal is to detect their possible resistance in time to help doctors make better decisions and treatment choices. It highlights two medicines: Anidulafungin reliable against Candida and Caspofungin. Additionally, a predictive model such as K-nearest neighbors (KNNs) is used to identify trends in drug resistance, which is useful in planning strategies for treatment.