BACKGROUND Cardiovascular diseases kill approximately 17 million people globally every year, and they mainly exhibit as myocardial infarctions and heart failures. Heart failure (HF) occurs when the heart cannot pump enough blood to meet the needs of the body.Available electronic medical records of patients quantify symptoms, body features, and clinical laboratory test values, which can be used to perform biostatistics analysis aimed at highlighting patterns and correlations otherwise undetectable by medical doctors. Machine learning, in particular, can predict patients' survival from their data and can individuate the most important features among those included in their medical records. METHODS In this paper, we analyze a dataset of 299 patients with heart failure collected in 2015. We apply several machine learning classifiers to both predict the patients survival, and rank the features corresponding to the most important risk factors. We also perform an alternative feature ranking analysis by employing traditional biostatistics tests, and compare these results with those provided by the machine learning algorithms. Since both feature ranking approaches clearly identify serum creatinine and ejection fraction as the two most relevant features, we then build the machine learning survival prediction models on these two factors alone. RESULTS Our results of these two-feature models show not only that serum creatinine and ejection fraction are sufficient to predict survival of heart failure patients from medical records, but also that using these two features alone can lead to more accurate predictions than using the original dataset features in its entirety. We also carry out an analysis including the follow-up month of each patient: even in this case, serum creatinine and ejection fraction are the most predictive clinical features of the dataset, and are sufficient to predict patients' survival. CONCLUSIONS This discovery has the potential to impact on clinical practice, becoming a new supporting tool for physicians when predicting if a heart failure patient will survive or not. Indeed, medical doctors aiming at understanding if a patient will survive after heart failure may focus mainly on serum creatinine and ejection fraction.

中文翻译：

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机器学习可以仅根据血清肌酐和射血分数来预测心力衰竭患者的生存率。

背景技术心血管疾病每年导致全球约1700万人死亡，主要表现为心肌梗塞和心力衰竭。当心脏无法泵出足够的血液来满足身体的需要时，就会发生心力衰竭（HF）。现有的患者电子病历可量化症状、身体特征和临床实验室测试值，可用于进行生物统计分析，旨在突出显示医生无法检测到的模式和相关性。尤其是机器学习，可以根据患者的数据预测患者的生存情况，并可以从患者的病历中找出最重要的特征。方法在本文中，我们分析了 2015 年收集的 299 名心力衰竭患者的数据集。我们应用多个机器学习分类器来预测患者的生存情况，并对与最重要的风险因素相对应的特征进行排序。我们还通过采用传统的生物统计学测试来执行替代特征排名分析，并将这些结果与机器学习算法提供的结果进行比较。由于两种特征排序方法都清楚地将血清肌酐和射血分数确定为两个最相关的特征，因此我们仅根据这两个因素构建了机器学习生存预测模型。结果我们对这两个特征模型的结果表明，不仅血清肌酐和射血分数足以根据病历预测心力衰竭患者的生存率，而且单独使用这两个特征可以比使用原始数据集做出更准确的预测其全部特点。我们还进行了包括每个患者的随访月份在内的分析：即使在这种情况下，血清肌酐和射血分数也是数据集最具预测性的临床特征，并且足以预测患者的生存。结论 这一发现有可能对临床实践产生影响，成为医生预测心力衰竭患者是否能生存的新支持工具。事实上，旨在了解患者在心力衰竭后是否能存活的医生可能主要关注血清肌酐和射血分数。