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首页> 外文会议>IEEE-EMBS International Conference on Biomedical and Health Informatics >Machine learning for predicting astigmatism in patients with keratoconus after intracorneal ring implantation
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Machine learning for predicting astigmatism in patients with keratoconus after intracorneal ring implantation

机译：机器学习预测角膜内环植入后圆锥角膜患者的散光

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摘要

This work proposes a new approach based on Machine Learning to predict astigmatism in patients with kera-toconus (KC) after ring implantation. KC is a non-inflamatory, progressive thinning disorder of the cornea, resulting in a protusion, myopia and irregular astigmatism. The intracorneal ring implantation surgery has become a suitable technique to deal with keratoconus without the need of a corneal transplant. Two machine learning (ML) classifiers based on artificial neural network and a decision tree were used in this work. Artificial neural networks performed better than decision trees, achieving an absolute mean error lower than 2 diopters in a validation data set. An analysis of the most relevant features was also carried out.
机译：这项工作提出了一种基于机器学习的新方法，以预测戒指植入后Kera-toconus（KC）患者散光。 KC是角膜的非巨大性渐进障碍，导致抗抗骨，近视和不规则的散光。鞘内环锭植入手术已成为处理角膜炎的合适技术，而无需角膜移植。基于人工神经网络和决策树的两种机器学习（ML）分类器在这项工作中使用。人工神经网络表现优于决策树，在验证数据集中实现低于2屈光度的绝对平均误差。还进行了对最相关的功能的分析。

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