The COVID-19 pandemic has presented unprecedented public health challenges worldwide. Understanding the factors contributing to COVID-19 mortality is critical for effective management and intervention strategies. This study aims to unlock the predictive power of data collected from personal, clinical, preclinical, and laboratory variables through machine learning (ML) analyses. A retrospective study was conducted in 2022 in a large hospital in Abadan, Iran. Data were collected and categorized into demographic, clinical, comorbid, treatment, initial vital signs, symptoms, and laboratory test groups. The collected data were subjected to ML analysis to identify predictive factors associated with COVID-19 mortality. Five algorithms were used to analyze the data set and derive the latent predictive power of the variables by the shapely additive explanation values. Results highlight key factors associated with COVID-19 mortality, including age, comorbidities (hypertension, diabetes), specific treatments (antibiotics, remdesivir, favipiravir, vitamin zinc), and clinical indicators (heart rate, respiratory rate, temperature). Notably, specific symptoms (productive cough, dyspnea, delirium) and laboratory values (D-dimer, ESR) also play a critical role in predicting outcomes. This study highlights the importance of feature selection and the impact of data quantity and quality on model performance. This study highlights the potential of ML analysis to improve the accuracy of COVID-19 mortality prediction and emphasizes the need for a comprehensive approach that considers multiple feature categories. It highlights the critical role of data quality and quantity in improving model performance and contributes to our understanding of the multifaceted factors that influence COVID-19 outcomes.

中文翻译：

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数据的预测能力：基于病例对照研究中的个人、临床、临床前和实验室变量对 Covid-19 死亡率进行机器学习分析

COVID-19 大流行给全世界带来了前所未有的公共卫生挑战。了解导致 COVID-19 死亡的因素对于有效的管理和干预策略至关重要。本研究旨在通过机器学习 (ML) 分析，释放从个人、临床、临床前和实验室变量收集的数据的预测能力。 2022年在伊朗阿巴丹的一家大型医院进行了一项回顾性研究。收集数据并分类为人口统计学、临床、合并症、治疗、初始生命体征、症状和实验室测试组。对收集的数据进行机器学习分析，以确定与 COVID-19 死亡率相关的预测因素。使用五种算法来分析数据集，并通过形状相加的解释值得出变量的潜在预测能力。结果强调了与 COVID-19 死亡率相关的关键因素，包括年龄、合并症（高血压、糖尿病）、具体治疗（抗生素、瑞德西韦、法匹拉韦、维生素锌）和临床指标（心率、呼吸频率、体温）。值得注意的是，特定症状（咳嗽、呼吸困难、谵妄）和实验室值（D-二聚体、ESR）在预测结果中也发挥着关键作用。这项研究强调了特征选择的重要性以及数据数量和质量对模型性能的影响。这项研究强调了机器学习分析在提高 COVID-19 死亡率预测准确性方面的潜力，并强调需要一种考虑多个特征类别的综合方法。它强调了数据质量和数量在提高模型性能方面的关键作用，并有助于我们了解影响 COVID-19 结果的多方面因素。