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A Comparative Study of Machine Learning Techniques for Caries Prediction

机译：机器学习技术对龋齿预测的比较研究

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There are striking disparities in the prevalence of dental disease by income. Poor children suffer twice as much dental caries as their more affluent peers, but are less likely to receive treatment. This paper presents an experimental study of the application of machine learning methods to the problem of caries prediction. For this paper a data set collected from interviews with children under five years of age, in 2006, in Recife, the capital of Pernambuco, a state in northeast Brazil, was built. Four different data mining techniques were applied to this problem and their results were confronted in terms of the classification error and area under the ROC curve (AUC). Results showed that the MLP neural network classifier out performed theother machine learning methods employed in the experiments, followed by the support vector machine (SVM) predictor. In addition, the results also show that some rules (extracted by decision tress) may be useful for understanding the most important factors that influence the occurrence of caries in children.
机译：牙科疾病患病率突出的差异受到收入。贫困儿童患有两倍的龋齿，因为他们更富裕的同龄人，但不太可能接受治疗。本文介绍了机器学习方法在龋齿预测问题中应用的实验研究。对于本文，建立了一份与五岁以下儿童的采访中收集的数据集，2006年在累积克拉南·巴西东北地区的国家的资本中。应用四种不同的数据挖掘技术对该问题应用，并且在ROC曲线（AUC）下的分类误差和面积方面遇到了它们的结果。结果表明，MLP神经网络分类器Out执行实验中采用的其他机器学习方法，其次是支持向量机（SVM）预测器。此外，结果还表明，一些规则（由决策发动机提取）可能是有用的，以了解影响儿童龋病的最重要的因素。

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