摘要
由一名新闻记者兼机器人与机器学习每日新闻编辑每日新闻-革兰氏阴性细菌L感染的新研究-钩端螺旋体病是一篇报道的主题。根据NewsRx编辑在巴西里约热内卢发表的新闻报道，研究表明，“钩端螺旋体病是一种全球性疾病，影响全世界的人，特别是潮湿和热带地区的人，并与严重的社会经济缺陷有关。它的症状经常与其他症状混淆，这可能会影响临床诊断和无法进行特定实验室测试。”我们的新闻记者从里约热内卢州立大学的研究中获得了一句话，“在这方面，本文介绍了预测钩端螺旋体病患者结局（治愈或死亡）的三种算法（决策树、随机森林和Adaboost）的研究。使用2007年至2017年政府国家侵略和通知系统（SINAN，葡萄牙语）中的记录，对于巴西的Par?州，该州使用了医疗保健、症状（头痛、呕吐、黄疸、小腿疼痛）和临床进化（肾衰竭和呼吸变化）的时间属性。在所选模型的性能评估中，观察到随机森林对训练数据集显示出90.81%的准确性，考虑到实验8的属性，因此，该结果考虑了实验10得出的最佳属性：首次症状就医、首次症状ELISA样本采集、入院时间、头颅HE、小腿疼痛、呕吐、黄疸、肾功能不全和呼吸系统改变。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Gram-Negative Bacteria l Infections - Leptospirosis is the subject of a report. According to news repor ting out of Rio de Janeiro, Brazil, by NewsRx editors, research stated, “Leptosp irosis is a global disease that impacts people worldwide, particularly in humid and tropical regions, and is associated with significant socio-economic deficien cies. Its symptoms are often confused with other syndromes, which can compromise clinical diagnosis and the failure to carry out specific laboratory tests.” Our news journalists obtained a quote from the research from Rio de Janeiro Stat e University, “In this respect, this paper presents a study of three algorithms (Decision Tree, Random Forest and Adaboost) for predicting the outcome (cure or death) of individuals with leptospirosis. Using the records contained in the gov ernment National System of Aggressions and Notification (SINAN, in portuguese) f rom 2007 to 2017, for the state of Par?, Brazil, where the temporal attributes o f health care, symptoms (headache, vomiting, jaundice, calf pain) and clinical e volution (renal failure and respiratory changes) were used. In the performance e valuation of the selected models, it was observed that the Random Forest exhibit ed an accuracy of 90.81% for the training dataset, considering the attributes of experiment 8, and the Decision Tree presented an accuracy of 74.2 9 for the validation database. So, this result considers the best attributes poi nted out by experiment 10: time first symptoms medical attention, time first sym ptoms ELISA sample collection, medical attention hospital admission time, headac he, calf pain, vomiting, jaundice, renal insufficiency, and respiratory alterati ons.”
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