On Development of Machine Learning Models with Aim of Medical Differential Diagnostics of the Comorbid States

2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) Pub Date : 2019-09-01 DOI:10.1109/IDAACS.2019.8924345

V. Martsenyuk, L. Babinets, Y. Dronyak, Olha Paslay, O. Veselska, K. Warwas, I. Andrushchak, A. Kłos-Witkowska

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引用次数: 3

Abstract

The purpose of the work is to develop mathematical and software background for the development of machine learning (ML) models in differential diagnostics of comorbid states. Flowchart includes basic steps of ML model development, including the statement of task, the choice of method (learner), setting its parameters and model assessment. The problems dealing with dimension reduction which arise often in differential diagnostics of comorbid states are highlighted and solved with help of modified PCA method. As an example we consider the problem of development of classifier for chronic pancreatitis combined with ascaridosis where we solve all tasks of ML model development. With help of benchmark of learners in the package mlr we compare different methods of ML when applying them in differential diagnostics of comorbid states.

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以共病状态医学鉴别诊断为目标的机器学习模型的发展

这项工作的目的是为共病状态的鉴别诊断中机器学习(ML)模型的开发开发数学和软件背景。流程图包括ML模型开发的基本步骤，包括任务的陈述、方法(学习者)的选择、参数的设置和模型的评估。重点讨论了共病状态鉴别诊断中常见的降维问题，并利用改进的主成分分析方法解决了降维问题。以慢性胰腺炎合并蛔虫病的分类器开发为例，解决了ML模型开发的所有任务。借助包mlr中的学习器基准，比较了不同的ML方法在共病状态鉴别诊断中的应用。

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2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS)

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