  Introduction- This paper mainly describes a way to detect with high accuracy
patients with early-stage Alzheimer's disease (ES-AD) versus healthy control
(HC) subjects, from datasets built with handwriting and drawing task records.
Method- The proposed approach uses subject's response times. An optimal subset
of tasks is first selected with a "Support Vector Machine" (SVM) associated
with a grid search. Mixtures of Gaussian distributions defined in the space of
task durations are then used to reproduce and explain the results of the SVM.
Finally, a surprisingly simple and efficient ad hoc classification algorithm is
deduced from the Gaussian mixtures. Results- The solution presented in this
paper makes two or even four times fewer errors than the best results of the
state of the art concerning the classification HC/ES-AD from handwriting and
drawing tasks. Discussion- The best SVM learning model reaches a high accuracy
for this classification but its learning capacity is too large to ensure a low
overfitting risk regarding the small size of the dataset. The proposed ad hoc
classification algorithm only requires to optimize three real-parameters. It
should therefore benefit from a good generalization ability.
