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Comparative analysis of machine learning methods for analyzing security practice in electronic health records’ logs.

机译：电子健康记录日志中安全实践分析机器学习方法的比较分析。

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Electronic health records (EHR) consists of broad, numerous and erratic accesses through self-authorizations and "brake the glass" scenarios. This is to fulfil the availability aspect of the the CIA (confidentiality, integrity) due to the time sensitive nature in healthcare especially during health emergency situations. Adversaries can use this as opportunity to illegitimately access patients records, thereby, compromising the entire EHR system.To avert this, a comparative analysis of machine learning classification methods was conducted with simulated EHR logs. The methods which were compared are Multinomial Naive Bayes(multnb), Bernoulli Naive Bayes (bernnb), Support Vector Machine (svm), Neural Network (nn), K-Nearest Neighbours(knn), Logistic Regression (lr), Random Forest (rf), and Decision Tree (dt).The experiment results show that all of the machine learning models used in this work performed very well for the role classification task but, Decision Tree (dt) and Random Forrest (rf) obtained the best result among all of the methods with the same accuracy value of 0.889 on all three datasets. For the anomaly detection task, generally, our proposed approach obtained a high recall and accuracy but low precision and F1-score. Soft Classification approach performed better than the Hard Classification approach. The best performance was achieved with Bernoulli Naive Bayes with none normalised data, with an F1-score of 0.893.
机译：电子健康记录（EHR）由自我授权和“制动玻璃”方案的广泛，众多和不稳定的访问组成。这是为了满足CIA（机密性，完整性）的可用性方面，因为医疗保健中的时间敏感性，特别是在健康紧急情况下。对手可以用这是非法访问患者记录的机会，从而损害整个EHR系统。避免这一点，通过模拟的EHR原木进行机器学习分类方法的比较分析。比较的方法是多项幼稚贝叶斯（Multnb），Bernoulli Naive Bayes（BernnB），支持向量机（SVM），神经网络（NN），K-Colless邻居（KNN），Logistic回归（LR），随机林（ RF）和决策树（DT）。实验结果表明，在这项工作中使用的所有机器学习模型对于角色分类任务而言，但是，决策树（DT）和随机FORREST（RF）获得了最佳结果在所有三个数据集中具有相同精度值0.889的方法中。对于异常检测任务，通常，我们所提出的方法获得了高召回和准确性，但精度低，精度和F1分数。软分类方法比硬分类方法更好。 Bernoulli Naive Bayes的最佳表现是无标准化数据的，F1分数为0.893。

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