慢性阻塞性肺疾病患者分类模型的意义

Mengyao Kang, Jiawei Zhao, Farnaz Farid
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摘要

基于机器学习的预测模型有潜力改造各种行业,其中一个有前途的领域是医疗保健。这项研究证明了机器学习在医疗保健方面的潜在影响,特别是在管理慢性阻塞性肺疾病(COPD)患者方面。实验结果展示了机器学习模型的卓越性能,超过了医生对COPD患者的预测。在评估的模型中,梯度提升决策树分类器表现最好,与医生的经验相比,它显示出卓越的分类准确性、精度、召回率和F1-Score。值得注意的是,最好的机器学习模型和医生的预测之间的比较揭示了一个有趣的模式:机器学习模型往往更保守,导致患者康复的可能性增加。
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Implications of Classification Models for Patients with Chronic Obstructive Pulmonary Disease
Machine learning-based prediction models have the potential to revamp various industries, and one such promising area is healthcare. This study demonstrates the potential impact of machine learning in healthcare, particularly in managing patients with Chronic Obstructive Pulmonary Disease (COPD). The experimental results showcase the remarkable performance of machine learning models, surpassing doctors' predictions for COPD patients. Among the evaluated models, the Gradient Boosted Decision Tree classifier emerges as the top performer, displaying exceptional classification accuracy, precision, recall, and F1-Score compared to doctors' experience. Notably, the comparison between the best machine learning model and doctors' predictions reveals an interesting pattern: machine learning models tend to be more conservative, resulting in an increased probability of patient recovery.
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