| DOI | Resolve DOI: https://doi.org/10.1142/S0218001419400135 |
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| Author | Search for: Belacel, Nabil1; Search for: Cuperlovic-Culf, Miroslava1 |
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| Affiliation | - National Research Council Canada. Digital Technologies
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| Format | Text, Article |
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| Subject | machine learning; PROAFTN; feature selection; metabolomics; Alzheimer's disease |
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| Abstract | Early and accurate Alzheimer’s disease (AD) diagnosis remains a challenge. Recently, increasing efforts have been focused towards utilization of metabolomics data for the discovery of biomarkers for screening and diagnosis of AD. Several machine learning approaches were explored for classifying the blood metabolomics profiles of cognitively healthy and AD patients. Differentiation between AD, mild cognitive impairment (MCI) and cognitively healthy subjects remains difficult. In this paper, we propose a new machine learning approach for the selection of a subset of features that provide an improvement in classification rates between these three levels of cognitive disorders. Our experimental results demonstrate that utilization of these selected metabolic markers improves the performance of several classifiers in comparison to the classification accuracy obtained for the complete metabolomics dataset. The obtained results indicate that our algorithms are effective in discovering a panel of biomarkers of AD and MCI from metabolomics data suggesting the possibility to develop a noninvasive blood diagnostic technique of AD and MCI. |
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| Publication date | 2019-03-19 |
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| Publisher | World Scientific |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | 8f34d0e7-afef-4e45-8fba-899a06e1c267 |
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| Record created | 2019-06-11 |
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| Record modified | 2020-03-16 |
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