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The rise of Alzheimers Disease worldwide has prompted a search for efficienttools which can be used to predict deterioration in cognitive decline leadingto dementia. In this paper, we explore the potential of survival machinelearning as such a tool for building models capable of predicting not onlydeterioration but also the likely time to deterioration. We demonstrate goodpredictive ability (0.86 C-Index), lending support to its use in clinicalinvestigation and prediction of Alzheimers Disease risk.

Predicting Alzheimers Disease Diagnosis Risk over Time with Survival Machine Learning on the ADNI Cohort