# Discriminating Pattern Mining for Diagnosing Reading Disorders +
## 4 Experiments
### Real Data  
As for the real case of study, we employ data collected and suitably anonymized by an Italian speech therapist center ([[http://www.logopediatherapeia.it](http://www.logopediatherapeia.it)]([http://www.logopediatherapeia.it](http://www.logopediatherapeia.it))). The tachistoscope used is that described in [[http://www.iflogos.it/tachistoscopio-flo](http://www.iflogos.it/tachistoscopio-flo)]([http://www.iflogos.it/tachistoscopio-flo](http://www.iflogos.it/tachistoscopio-flo)), since this tool has been developed by the same authors of the present article; in any case, each tachistoscope pro  
Figure 2: Behavior of the technique with respect to standard nmmf methods in terms of _Cost of Approximation_ (**a,c**), and _Discriminating Power_ (**b,d**), by changing the number of words (**a,b**), and the number of features (**c,d**). viding the same information as output can be employed as input module of the presented technique, in particular. For each patient, the data were collected in three consecutive sessions, and each session provided 75 words. Thus, we have datasets consisting of 225 words. We started by the groups of two letters individuated by our technique as discriminative and we gained the confirmation by domain experts of disorders related with these pairs. To prove the ability of the method in individuating interesting pairs, we perform the following experiments. We, first, isolate the words containing the top exceptional pairs. Then, we build a dataset \(S\) with the other words and shuffle it. Next, we build the two datasets \(W_{e}\) and \(W_{r}\) with the words coming from \(S\). Iteratively, we inject words containing exceptional pairs in the two datasets with different percentages and measure the rank of position of the exceptional pairs, namely we order the entry of the prototypes \(\mathcal{P}_{e}^{s}\) and \(\mathcal{P}_{r}^{s}\) and measure the number of times in which the exceptional pair is in the first position. Figure 3a reports the results of this experiment. Figure shows that the technique is able to mine a pair of letters just if they have a discriminating power. Indeed, as the occurrence of the pair reaches the 50%, namely \(W_{e}\) and \(W_{r}\) are similar with respect to the pair, and the technique does not mine the pair anymore. Vice versa, as for the standard _nmnf_ method, it is not influenced by cross occurrences and then, since the pair is relevant at least in one dataset, it is detected almost always in the same way. A second part of experiments on real data has concerned the study of the behavior of the proposed technique on recognizing known pathologies. In particular, the algorithm has been fed with datasets referred to individuals with a single known pathology previously diagnosed by experts. In this scenario, the accuracy of the method is measured and results are reported in Figure 3b. There are different pathologies generating different typologies of errors during tachisoscope sessions, among them  
* _visual dyslexia_;
* _superficial dyslexia_ where lexical pathways are compromised, but reading, although difficult, is possible;
* _phonological dyslexia_ where a phonological path is compromised since a correct association between grapheme and phoneme is missing;
* _deep dyslexia_ where the semantic path is compromised, and semantic paraphasias are performed. * _dysletic dyslexia_ where the representation of the word in its variations is difficult, and the new words are not understandable;
* _dysphonological dyslexia_ concerning a deficit at the level of grapheme phoneme mappings. Figure 4 reports accuracy achieved on recognizing such pathologies. As expected, _visual dyslexia_ and _superficial dyslexia_ are a bit confused since they share some characteristics;  
Figure 3: Experiments on real data. (**a**) Behavior of discriminative pairs. (**b**) Behavior on known pathologies. conversely, _dysletic dyslexia_ is the easier to be recognized due to the features related to non-existing words.