Abstract
Alzheimer’s Disease (AD) is the most prevalent progressive neurodegenerative disorder of the elderly. Prospective treatments for slowing down or pausing the process of AD require identification of the disease at an early stage. Many patients with mild cognitive impairment (MCI) may eventually develop AD. In this study, we evaluate the significance of using longitudinal data for efficiently predicting MCI-to-AD conversion a few years ahead of clinical diagnosis. The use of longitudinal data is generally restricted due to missing feature readings. We implement five different techniques to compute missing feature values of neuropsychological predictors of AD. We use two different summary measures to represent the artificially completed longitudinal features. In a comparison with other recent techniques, our work presents an improved accuracy of 71.16 % in predicting pre-clinical AD. These results prove feasibility of building AD staging and prognostic systems using longitudinal data despite the presence of missing values.
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Acknowledgements
We would like to thank all investigators of ADNI listed at: https://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowedge-ment_List.pdf, for developing and making their data publically available.
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Minhas, S., Khanum, A., Riaz, F., Alvi, A., Khan, S.A., Alzheimer’s Disease Neuroimaging Initiative. (2015). Early Alzheimer’s Disease Prediction in Machine Learning Setup: Empirical Analysis with Missing Value Computation. In: Jackowski, K., Burduk, R., Walkowiak, K., Wozniak, M., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2015. IDEAL 2015. Lecture Notes in Computer Science(), vol 9375. Springer, Cham. https://doi.org/10.1007/978-3-319-24834-9_49
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DOI: https://doi.org/10.1007/978-3-319-24834-9_49
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