Abstract
Nowadays, 35 million people worldwide suffer from some form of dementia. Given the increase in life expectancy it is estimated that in 2035 this number will grow to 115 million. Alzheimer’s disease is the most common cause of dementia and it is of great importance diagnose it at an early stage. This is the main goal of this work, the development of a new automatic method to predict the mild cognitive impairment (MCI) patients who will develop Alzheimer’s disease within one year or, conversely, its impairment will remain stable. This technique will analyze data from both magnetic resonance imaging and neuropsychological tests by utilizing a t-test for feature selection, maximum-uncertainty linear discriminant analysis (MLDA) for classification and leave-one-out cross validation (LOOCV) for evaluating the performance of the methods, which achieved a classification accuracy of 73.95 %, with a sensitivity of 72.14 % and a specificity of 73.77 %.
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Acknowledgments
This work was partly supported by the MICINN under the TEC2012-34306 project and the Consejería de Innovación, Ciencia y Empresa (Junta de Andalucía, Spain) under the Excellence Project P11-TIC-7103.
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Arco, J.E., Ramírez, J., Górriz, J.M., Puntonet, C.G., Ruz, M. (2016). Short-term Prediction of MCI to AD Conversion Based on Longitudinal MRI Analysis and Neuropsychological Tests. In: Chen, YW., Torro, C., Tanaka, S., Howlett, R., C. Jain, L. (eds) Innovation in Medicine and Healthcare 2015. Smart Innovation, Systems and Technologies, vol 45. Springer, Cham. https://doi.org/10.1007/978-3-319-23024-5_35
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