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Comparing Logistic Regression Models with Alternative Machine Learning Methods to Predict the Risk of Drug Intoxication Mortality

机译：将Logistic回归模型与替代机器学习方法进行比较，以预测药物中毒死亡率的风险

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(1) Medical research has shown an increasing interest in machine learning, permitting massive multivariate data analysis. Thus, we developed drug intoxication mortality prediction models, and compared machine learning models and traditional logistic regression. (2) Categorized as drug intoxication, 8,937 samples were extracted from the Korea Centers for Disease Control and Prevention (2008-2017). We trained, validated, and tested each model through data and compared their performance using three measures: Brier score, calibration slope, and calibration-in-the-large. (3) A chi-square test demonstrated that mortality risk statistically significantly differed according to severity, intent, toxic substance, age, and sex. The multilayer perceptron model (MLP) had the highest area under the curve (AUC), and lowest Brier score in training and validation phases, while the logistic regression model (LR) showed the highest AUC (0.827) and lowest Brier score (0.0307) in the testing phase. MLP also had the second-highest AUC (0.816) and second-lowest Brier score (0.003258) in the testing phase, demonstrating better performance than the decision-making tree model. (4) Given the complexity of choosing tuning parameters, LR proved competitive when using medical datasets, which require strict accuracy.
机译：（1）医学研究表明，对机器学习的兴趣越来越大，允许大规模多变量数据分析。因此，我们开发了药物中毒死亡率预测模型，并比较了机器学习模型和传统的逻辑回归。 （2）作为药物中毒分类，从韩国疾病控制和预防中心提取8,937个样品（2008-2017）。我们通过数据训练，验证并测试了每个模型，并使用三种措施进行了比较：Brier得分，校准斜率和校准而校准。 （3）Chi-Square测试表明，根据严重程度，意图，有毒物质，年龄和性别，死亡率风险统计学显着不同。 Multidayer Perceptron模型（MLP）的曲线（AUC）下的最高面积，训练和验证阶段的最低档次评分，而逻辑回归模型（LR）显示了最高的AUC（0.827）和最低的BRIER得分（0.0307）在测试阶段。在测试阶段，MLP还具有第二最高AUC（0.816）和第二最低档次（0.003258），表明比决策树模型更好的性能。 （4）鉴于选择调谐参数的复杂性，在使用医疗数据集时，LR证明了竞争力，这需要严格的准确性。

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