English(EN) Evaluating LLM-Generated Rules for Heart Disease Prediction

LLM生成的心脏病预测规则准确性落后于传统模型

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-15 04:00

一项发表在arXiv上的新研究评估了像GPT-4o和Claude Sonnet 4.6这样的大型语言模型（LLMs）在生成预测心脏病的规则方面的有效性。研究发现，与LLM生成的基于规则的系统相比，传统的机器学习模型（如Random Forest和Naive Bayes）在准确性和预测性能方面表现明显更优。尽管LLM生成的规则预测能力较弱，但它们提供了增强的可解释性和透明度，在医疗AI中呈现了性能与可解释性之间的权衡。 AI

影响强调了LLM在复杂预测任务中的当前局限性，并突出了医疗AI中对可解释性的需求。

排序理由学术论文，比较LLM生成的规则与传统机器学习模型。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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arXiv cs.LG TIER_1 English(EN) · Feisal Alaswad, Batoul Aljaddouh, Maher Alrahhal, Wafaa Al Nassan, Talal Bonn · 2026-09-15 04:00

评估LLM生成的用于心脏病预测的规则

arXiv:2609.13192v1 Announce Type: new Abstract: This study compares traditional machine learning models and Large Language Model (LLM)-generated rule-based systems for heart disease prediction using the UCI Heart Disease dataset. Several classifiers, including Logistic Regression…

本文如何被排名

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评估LLM生成的用于心脏病预测的规则

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