| Autor: |
Bosnić, Zoran, Bratić, Brankica, Ivanović, Mirjana, Semnic, Marija, Oder, Iztok, Kurbalija, Vladimir, Vujanić Stankov, Tijana, Bugarski Ignjatović, Vojislava |
| Předmět: |
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| Zdroj: |
Journal of Experimental & Theoretical Artificial Intelligence; Dec 2021, Vol. 33 Issue 6, p1015-1032, 18p |
| Abstrakt: |
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. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
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