Abstract Data mining techniques have been successfully utilized in different applications of significant fields, including medical research. With the wealth of data available within the health-care systems, there is a lack of practical analysis tools to discover hidden relationships and trends in data. The complexity of medical data that is unfavorable for most models is a considerable challenge in prediction. The ability of a model to perform accurately and efficiently in disease diagnosis is extremely significant. Thus, the model must be selected to fit the data better, such that the learning from previous data is most efficient, and the diagnosis of the disease is highly accurate. This work is motivated by the limited number of regression analysis tools for multivariate counts in the literature. We propose two regression models for count data based on flexible distributions, namely, the multinomial Beta-Liouville and multinomial scaled Dirichlet, and evaluated the proposed models in the problem of disease diagnosis. The performance is evaluated based on the accuracy of the prediction which depends on the nature and complexity of the dataset. Our results show the efficiency of the two proposed regression models where the prediction performance of both models is competitive to other previously used regression models for count data and to the best results in the literature.

中文翻译：

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计数数据的基于分布的灵活回归模型：在医学诊断中的应用

摘要 数据挖掘技术已成功应用于重要领域的不同应用，包括医学研究。由于医疗保健系统中有大量可用数据，因此缺乏实用的分析工具来发现数据中隐藏的关系和趋势。对大多数模型不利的医疗数据的复杂性是预测中的一个相当大的挑战。模型在疾病诊断中准确有效地执行的能力非常重要。因此，必须选择模型以更好地拟合数据，以便从以前的数据中学习最有效，并且疾病的诊断高度准确。这项工作的动机是文献中用于多元计数的回归分析工具数量有限。我们提出了两种基于灵活分布的计数数据回归模型，即多项 Beta-Liouville 和多项标度 Dirichlet，并在疾病诊断问题中评估了所提出的模型。性能是根据预测的准确性来评估的，预测的准确性取决于数据集的性质和复杂性。我们的结果显示了两个提出的回归模型的效率，其中两个模型的预测性能与其他以前使用的计数数据回归模型和文献中的最佳结果相比具有竞争力。性能是根据预测的准确性来评估的，预测的准确性取决于数据集的性质和复杂性。我们的结果显示了两个提出的回归模型的效率，其中两个模型的预测性能与其他以前使用的计数数据回归模型和文献中的最佳结果相比具有竞争力。性能是根据预测的准确性来评估的，预测的准确性取决于数据集的性质和复杂性。我们的结果显示了两个提出的回归模型的效率，其中两个模型的预测性能与其他以前使用的计数数据回归模型和文献中的最佳结果相比具有竞争力。