ABSTRACT

Improvement of prediction accuracy and early detection of the Alzheimer’s disease is becoming increasingly important for managing its impact on lives of affected patients. Many machine learning approaches have been applied to support the diagnosis and prediction of this illness. In this paper we propose an approach for improving the Alzheimer’s disease classification accuracy by using data fusion of several independent clinical datasets. Data fusion was performed twofold: 1) by enriching attributes of the base dataset with the attributes of the secondary dataset and 2) by enriching the examples set of the base dataset with the examples of the secondary dataset. In both cases the missing values (for newly added attributes and/or examples) were predicted by using linear regression for numeric and naive Bayes classifier for nominal attributes. We experimented on three data sources: on a dataset of Alzheimer’s disease-impaired patients, on a dataset of patients with vascular dementia, and on a dataset of patients who have been affected by a stroke. We fused these datasets with different data fusion approaches and analysed the improvement in classification accuracy as well as the quality of the fused attributes. The experiments indicated that we obtained an increase of classification accuracy on the fused dataset compared with the accuracy obtained from individual dataset.

摘要

提高阿尔茨海默病的预测准确性和早期检测对于管理其对受影响患者生活的影响变得越来越重要。许多机器学习方法已被应用于支持这种疾病的诊断和预测。在本文中，我们提出了一种通过使用多个独立临床数据集的数据融合来提高阿尔茨海默病分类准确性的方法。数据融合是双重执行的：1）通过使用辅助数据集的属性丰富基础数据集的属性，以及 2）通过使用辅助数据集的示例丰富基础数据集的示例集。在这两种情况下，缺失值（对于新添加的属性和/或示例）都是通过对数字使用线性回归和对名义属性使用朴素贝叶斯分类器来预测的。我们对三个数据源进行了实验：阿尔茨海默病患者数据集、血管性痴呆患者数据集和中风患者数据集。我们将这些数据集与不同的数据融合方法融合在一起，并分析了分类精度的提高以及融合属性的质量。实验表明，与从单个数据集获得的精度相比，我们在融合数据集上获得了分类精度的提高。我们将这些数据集与不同的数据融合方法融合在一起，并分析了分类精度的提高以及融合属性的质量。实验表明，与从单个数据集获得的精度相比，我们在融合数据集上获得了分类精度的提高。我们将这些数据集与不同的数据融合方法融合在一起，并分析了分类精度的提高以及融合属性的质量。实验表明，与从单个数据集获得的精度相比，我们在融合数据集上获得了分类精度的提高。