  Developmental Dyslexia (DD) is a learning disability related to the
acquisition of reading skills that affects about 5% of the population. DD can
have an enormous impact on the intellectual and personal development of
affected children, so early detection is key to implementing preventive
strategies for teaching language. Research has shown that there may be
biological underpinnings to DD that affect phoneme processing, and hence these
symptoms may be identifiable before reading ability is acquired, allowing for
early intervention. In this paper we propose a new methodology to assess the
risk of DD before students learn to read. For this purpose, we propose a mixed
neural model that calculates risk levels of dyslexia from tests that can be
completed at the age of 5 years. Our method first trains an auto-encoder, and
then combines the trained encoder with an optimized ordinal regression neural
network devised to ensure consistency of predictions. Our experiments show that
the system is able to detect unaffected subjects two years before it can assess
the risk of DD based mainly on phonological processing, giving a specificity of
0.969 and a correct rate of more than 0.92. In addition, the trained encoder
can be used to transform test results into an interpretable subject spatial
distribution that facilitates risk assessment and validates methodology.
