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Antibiotic resistance is a significant challenge in medicine, stemming from the misuse of antibiotics and resulting in drug-resistant bacteria. To address this, it is crucial to identify appropriate treatment options, including Over-the-Counter (OTC) medications and doctor consultations, to minimize unnecessary healthcare costs while effectively managing antibiotic resistance. Developing a machine learning-based prediction model for OTC medications and doctor consultations becomes imperative. The research utilizes patient demographic data, medical histories, and symptom data labeled with OTC drug usage or doctor consultations from CMED, a large healthcare system in Bangladesh. An ensemble model has been created to predict treatment options, achieving an accuracy of 94%, precision of 91%, and an impressive ROC value of 0.98 in determining whether patients should opt for OTC drugs or seek doctor consultations. These findings highlight the value of machine learning in optimizing patient care and healthcare outcomes, guiding patients to appropriate care and reducing the excessive use of antibiotics. This approach holds the potential to significantly impact the development of antibiotic-resistant bacteria and improve patient outcomes.

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An AI-Based Clinical Recommendation System Using Ensemble-Based Soft Voting Classifier

  • 会议论文

  • 发表日期：

    2025年

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  • Tabia Tanzin Prama,
  • Marzia Zaman,
  • Farhana Sarkar,
  • Khondaker A. Mamun

摘要

Antibiotic resistance is a significant challenge in medicine, stemming from the misuse of antibiotics and resulting in drug-resistant bacteria. To address this, it is crucial to identify appropriate treatment options, including Over-the-Counter (OTC) medications and doctor consultations, to minimize unnecessary healthcare costs while effectively managing antibiotic resistance. Developing a machine learning-based prediction model for OTC medications and doctor consultations becomes imperative. The research utilizes patient demographic data, medical histories, and symptom data labeled with OTC drug usage or doctor consultations from CMED, a large healthcare system in Bangladesh. An ensemble model has been created to predict treatment options, achieving an accuracy of 94%, precision of 91%, and an impressive ROC value of 0.98 in determining whether patients should opt for OTC drugs or seek doctor consultations. These findings highlight the value of machine learning in optimizing patient care and healthcare outcomes, guiding patients to appropriate care and reducing the excessive use of antibiotics. This approach holds the potential to significantly impact the development of antibiotic-resistant bacteria and improve patient outcomes.