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This study explores the integration of Random Forest classifiers, Large Language Models (LLMs), and Natural Language Processing (NLP) to improve diagnostic performance in the analysis of chest X-rays using the Indiana University dataset. Utilizing advanced NLP techniques, the research preprocesses textual data from radiological reports to extract key features, which are then merged with image-derived data. This improved dataset is analyzed with Random Forest classifiers to predict specific clinical outcomes, focusing on the detection of medical conditions and the estimation of case urgency. The findings discover that the combination of NLP, LLMs, and machine learning not only increases diagnostic precision but also reliability, especially in identifying critical conditions quickly. With 99.35% accuracy, the model shows dramatic leaps over traditional diagnostic methods. The results identify machine learning’s strong potential in medical imaging, suggesting that by providing faster and more accurate diagnostic predictions, the technologies can greatly improve clinical judgment and patient care.

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Predictive Analysis of Chest X-rays Using NLP and Large Language Models with the Indiana University Dataset and Random Forest Classifier

  • 会议论文

  • 发表日期：

    2026年

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  • Azita Ramezani,
  • Bahareh Sanabakhsh

摘要

This study explores the integration of Random Forest classifiers, Large Language Models (LLMs), and Natural Language Processing (NLP) to improve diagnostic performance in the analysis of chest X-rays using the Indiana University dataset. Utilizing advanced NLP techniques, the research preprocesses textual data from radiological reports to extract key features, which are then merged with image-derived data. This improved dataset is analyzed with Random Forest classifiers to predict specific clinical outcomes, focusing on the detection of medical conditions and the estimation of case urgency. The findings discover that the combination of NLP, LLMs, and machine learning not only increases diagnostic precision but also reliability, especially in identifying critical conditions quickly. With 99.35% accuracy, the model shows dramatic leaps over traditional diagnostic methods. The results identify machine learning’s strong potential in medical imaging, suggesting that by providing faster and more accurate diagnostic predictions, the technologies can greatly improve clinical judgment and patient care.