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<p>This study compared the performance of classical feature-based machine learning models (CMLs) and large language models (LLMs) in predicting COVID-19 mortality using high-dimensional tabular data from 9,134 patients across four hospitals. Seven CML models, including XGBoost and random forest (RF), were evaluated alongside eight LLMs, such as GPT-4 and Mistral-7b, which performed zero-shot classification on text-converted structured data. Additionally, Mistral-7b was fine-tuned using the QLoRA approach. XGBoost and RF demonstrated superior performance among CMLs, achieving F1 scores of 0.87 and 0.83 for internal and external validation, respectively. GPT-4 led the LLM category with an F1 score of 0.43, while fine-tuning Mistral-7b significantly improved its recall from 1% to 79%, yielding a stable F1 score of 0.74 during external validation. Although LLMs showed moderate performance in zero-shot classification, fine-tuning substantially enhanced their effectiveness, potentially bridging the gap with CML models. However, CMLs still outperformed LLMs in handling high-dimensional tabular data tasks. This study highlights the potential of both CMLs and fine-tuned LLMs in medical predictive modeling, while emphasizing the current superiority of CMLs for structured data analysis.</p>

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Large language models versus classical machine learning performance in COVID-19 mortality prediction using high-dimensional tabular data

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月28日

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  • Mohammadreza Ghaffarzadeh-Esfahani,
  • Mahdi Ghaffarzadeh-Esfahani,
  • Aryan Salahi-Niri,
  • Hossein Toreyhi,
  • Zahra Atf,
  • Amirali Mohsenzadeh-Kermani,
  • Mahshad Sarikhani,
  • Zohreh Tajabadi,
  • Fatemeh Shojaeian,
  • Mohammad Hassan Bagheri,
  • Aydin Feyzi,
  • Mohamadamin Tarighat-Payma,
  • Narges Gazmeh,
  • Fateme Heydari,
  • Hossein Afshar,
  • Amirreza Allahgholipour,
  • Farid Alimardani,
  • Ameneh Salehi,
  • Naghmeh Asadimanesh,
  • Mohammad Amin Khalafi,
  • Hadis Shabanipour,
  • Ali Moradi,
  • Sajjad Hossein Zadeh,
  • Omid Yazdani,
  • Romina Esbati,
  • Moozhan Maleki,
  • Danial Samiei Nasr,
  • Amirali Soheili,
  • Hossein Majlesi,
  • Saba Shahsavan,
  • Alireza Soheilipour,
  • Nooshin Goudarzi,
  • Erfan Taherifard,
  • Hamidreza Hatamabadi,
  • Jamil S. Samaan,
  • Thomas Savage,
  • Ankit Sakhuja,
  • Ali Soroush,
  • Girish Nadkarni,
  • Ilad Alavi Darazam,
  • Mohamad Amin Pourhoseingholi,
  • Seyed Amir Ahmad Safavi-Naini

摘要

This study compared the performance of classical feature-based machine learning models (CMLs) and large language models (LLMs) in predicting COVID-19 mortality using high-dimensional tabular data from 9,134 patients across four hospitals. Seven CML models, including XGBoost and random forest (RF), were evaluated alongside eight LLMs, such as GPT-4 and Mistral-7b, which performed zero-shot classification on text-converted structured data. Additionally, Mistral-7b was fine-tuned using the QLoRA approach. XGBoost and RF demonstrated superior performance among CMLs, achieving F1 scores of 0.87 and 0.83 for internal and external validation, respectively. GPT-4 led the LLM category with an F1 score of 0.43, while fine-tuning Mistral-7b significantly improved its recall from 1% to 79%, yielding a stable F1 score of 0.74 during external validation. Although LLMs showed moderate performance in zero-shot classification, fine-tuning substantially enhanced their effectiveness, potentially bridging the gap with CML models. However, CMLs still outperformed LLMs in handling high-dimensional tabular data tasks. This study highlights the potential of both CMLs and fine-tuned LLMs in medical predictive modeling, while emphasizing the current superiority of CMLs for structured data analysis.