婴儿死亡率的特点是一岁以下幼儿死亡，这是一个影响世界上数百万儿童的问题。本文的目的是在数据库中使用知识发现的概念，特别是数据挖掘阶段的机器学习，描述巴西两个州的婴儿死亡率：圣卡塔琳娜，婴儿死亡率是该国各州最低的，和阿马帕,最高的。分类器C4.5、JRip、随机森林,SVM,使用多层感知器，并对两种状态下分类器获得的结果进行了简要比较。此外,数据集预处理是详细的，其中包括属性选择和类平衡。结果表明，APGAR5、WEIGHT、和先天性异常从基于树的分类器生成的规则中脱颖而出。
Infant mortality is characterized by the death of young children under the age of one, and it is an issue affecting millions of children in the world. The objective of this article is to employ concepts of knowledge discovery in databases, specifically of machine learning in the data mining phase, to characterize infant mortality in two states of Brazil: Santa Catarina, with the lowest infant mortality rate of the country\'s states, and Amapá, with the highest. The classifiers C4.5, JRip, Random Forest, SVM, and Multilayer Perceptron were used, and a brief comparison of the results obtained by the classifiers in both states is made. In addition, the dataset preprocessing is detailed, which includes attribute selection and class balancing. The results show that the features APGAR5, WEIGHT, and CONGENITAL ANOMALY stood out the most from the rules generated by the tree-based classifiers.
