Automatic detection of developmental dyslexia from eye movement data
Lukemisen erityisvaikeus eli dysleksia on maailmanlaajuisesti yleisin neurologinen oppimisvaikeus. Se voi hoitamattomana merkittävästi haitata yksilön akateemista menestystä. Erityisvaikeuden tunnistaminen ja hoitaminen aikaisessa vaiheessa voi kuitenkin vähentää huomattavasti häiriön aiheuttamia ongelmia. Tässä tutkimuksessa esitetään menetelmä tunnistaa dysleksia koneoppimisen avulla silmänliikedatasta. Hyödyntämällä suunnittelutieteen periaatteita oli mahdollista saada uutta tietoa käytettyyn aineistoon liittyen sekä luoda koneoppimismalli, joka pystyy luotettavasti tunnistamaan lukemisen erityisvaikeudesta kärsivät henkilöt. Tutkimuksessa käytettiin tukivektorikone- ja satunnaismetsä-menetelmiä ennustavien mallien luomiseksi. Parhaan saadun mallin tunnistamisen yleistarkkuus oli 89,8% ja dyslektikkojen tunnistamisen tarkkuus 75,9%. Dyslexia is the most common neurological learning disability found worldwide. Though it can seriously hinder individuals' academic success, detecting and treating it early on can drastically reduce its negative effect. Detecting dyslexia reliably and with ease is thus of paramount importance. In this thesis, a method using machine learning and eye movement data to predict if the reader has dyslexia is presented. By using the design science approach, it was possible to obtain new information regarding the data used in addition to a model capable of reliably predicting reading disorders. Support Vector Machine and Random Forest were the methods studied and applied to the data. The best model was obtained by the Support Vector Machine classifier using Random Forest to select the most important features: the general accuracy achieved was 89.8% and the accuracy of detecting dyslexics was 75.9%.
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