医疗文本分类机器学习方法研究

A Study of Machine Learning Algorithms Applied to Medical Text Classification

为了解决看病难、看病贵的问题,在人工智能的指导下,研发出远程医疗机器人智能导诊系统。其中,机器学习算法扮演着重要的角色,它相当于医生的大脑。本文将对医疗领域常用的机器学习方法进行研究,包括:决策树、神经网络、贝叶斯以及k邻近方法等。通过比较各种机器学习算法在医疗领域的分类效果,发现朴素贝叶斯算法用于疾病诊断算法的效果更好。

In order to solve the difficult medical services and expensive medical cost,we design a telemedicine robot with the help of artificial intelligence.The machine learning algorithm plays an important role,which is equivalent to the doctor's brain.In this paper,we study machine learning algorithms applied to medical text classification,decision tree,artificial neural networks,bayes,k_NN and so on.By comparing the classification results of various machine learning algorithms in medical field,Naive Bayesian algorithm is more effective for diagnosing disease.