摘要
一位新闻记者兼机器人与机器学习每日新闻编辑每日新闻-心脏病和糖尿病的新研究-心力衰竭是一篇报道的主题。根据NewsRx编辑在波兰格但斯克的新闻报道，研究表明，"根据欧洲心脏病学会的数据，全球早期心力衰竭患者的数量翻了一番，从1990年的3350万人增加到2017年的6430万人，并预计在这十年内将进一步大幅增加。纽约心脏协会（NY HA）功能分类是内科医生最常用的心力衰竭分类系统之一。我们的新闻记者从格但斯克科技大学的研究中获得了一句话：“每一个NYHA类别描述了患者在进行体育活动时的症状，提供了一个强有力的心脏表现指标。在每一种情况下，NYHA类别都是根据治疗医生的子目标评估而常规确定的。然而，这种诊断可能存在偏差。”为了解决这一问题，我们利用机器学习方法开发了决策树和一组决策规则，作为盲态研究者进行无偏评估的额外工具。在包含434个观测值的数据集上，我们首先采用监督学习方法训练决策树模型。在随后的阶段，采用集成学习技术对投票分类器和随机森林模型进行了设计,并采用分层交叉验证的方法对一个ll模型进行了性能评估,决策树、随机森林和投票分类器模型的准确率分别为76.28%、96.77%、96.77%。结果表明，随机森林和投票分类器对NYHA I和II的分类准确率分别为98.7%和100%，而对于NYHA IV，随机森林和投票分类器对NYHA II的分类准确率均为100%，而投票分类器对NYHA IV的分类准确率分别为90%和90%，决策树的分类效果最差。就其在临床实践中的支持作用而言，结果似乎令人满意。特别是，使用机器学习工具可以减少甚至消除医生评估中的偏见。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Heart Disorders and Di seases - Heart Failure is the subject of a report. According to news reporting o ut of Gdansk, Poland, by NewsRx editors, research stated, “According to the Euro pean Society of Cardiology, globally the number of patients with heart failure n early doubled from 33.5 million in 1990 to 64.3 million in 2017, and is further projected to increase dramatically in this decade, still remaining a leading cau se of morbidity and mortality. One of the most frequently applied heart failure classification systems that physicians use is the New York Heart Association (NY HA) Functional Classification.” Our news journalists obtained a quote from the research from the Gdansk Universi ty of Technology, “Each NYHA class describes a patient’s symptoms while performi ng physical activities, delivering a strong indicator of the heart performance. In each case, a NYHA class is individually determined routinely based on the sub jective assessment of the treating physician. However, such diagnosis can suffer from bias, eventually affecting a valid assessment. To tackle this issue, we ta ke advantage of the machine learning approach to develop a decision-tree, along with a set of decision rules, which can serve as additional blinded investigator tool to make unbiased assessment. On a dataset containing 434 observations, the supervised learning approach was initially employed to train a Decision Tree mo del. In the subsequent phase, ensemble learning techniques were utilized to deve lop both the Voting Classifier and the Random Forest model. The performance of a ll models was assessed using 10-fold cross-validation with stratification.The De cision Tree, Random Forest, and Voting Classifier models reported accuracies of 76.28%, 96.77%, and 99.54 % respectively. The Voting Classifier led in classifying NYHA I and III with 98.7% and 100% accuracy. Both Random Forest and Voting Classifier flawle ssly classified NYHA II at 100%. However, for NYHA IV, Random Fores t achieved a perfect score, while the Voting Classifier reported 90% . The Decision Tree showed the least effectiveness among all the models tested. In our opinion, the results seem satisfactory in terms of their supporting role in clinical practice. In particular, the use of a machine learning tool could re duce or even eliminate the bias in the physician’s assessment.”
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