Abstract：
Leptospirosis is a global disease that impacts people worldwide, particularly in humid and tropical regions, and is associated with significant socio-economic deficiencies. Its symptoms are often confused with other syndromes, which can compromise clinical diagnosis and the failure to carry out specific laboratory tests. 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 government National System of Aggressions and Notification (SINAN, in portuguese) from 2007 to 2017, for the state of Pará, Brazil, where the temporal attributes of health care, symptoms (headache, vomiting, jaundice, calf pain) and clinical evolution (renal failure and respiratory changes) were used. In the performance evaluation of the selected models, it was observed that the Random Forest exhibited an accuracy of 90.81% for the training dataset, considering the attributes of experiment 8, and the Decision Tree presented an accuracy of 74.29 for the validation database. So, this result considers the best attributes pointed out by experiment 10: time first symptoms medical attention, time first symptoms ELISA sample collection, medical attention hospital admission time, headache, calf pain, vomiting, jaundice, renal insufficiency, and respiratory alterations. The contribution of this article is the confirmation that artificial intelligence, using the Decision Tree model algorithm, depicting the best choice as the final model to be used in future data for the prediction of human leptospirosis cases, helping in the diagnosis and course of the disease, aiming to avoid the evolution to death.
摘要：
钩端螺旋体病是一种全球性疾病，影响着全世界的人们，特别是在潮湿和热带地区，并与重大的社会经济缺陷有关。它的症状经常与其他综合征混淆，这可能会损害临床诊断和无法进行特定的实验室测试。在这方面,本文研究了三种算法(决策树，随机森林和Adaboost）用于预测钩端螺旋体病个体的结局（治愈或死亡）。使用政府国家进攻和通知系统中包含的记录(SINAN，葡萄牙语)从2007年到2017年，对于帕拉州，巴西,医疗保健的时间属性，症状（头痛，呕吐,黄疸,使用小腿疼痛）和临床演变（肾衰竭和呼吸变化）。在选定模型的性能评估中，据观察，随机森林对训练数据集的准确率为90.81%，考虑到实验8的属性，决策树对验证数据库的准确度为74.29。所以,这个结果考虑了实验10指出的最佳属性:第一症状医疗护理的时间，时间第一个症状ELISA样本收集，医疗注意入院时间，头痛,小腿疼痛，呕吐,黄疸,肾功能不全,和呼吸改变。这篇文章的贡献是证实了人工智能，使用决策树模型算法，将最佳选择描绘为未来数据中用于预测人类钩端螺旋体病病例的最终模型，有助于疾病的诊断和病程，旨在避免进化到死亡。
