Abstract:
Analysis of dyslexia is an interesting research area in medical field through MRI
scans and eye ball tracking etc and also it is very interesting research in machine
learning through different models. Dyslexia is a learning difficulty that affects the
ability of reading, writing, listening and learning in children and adults. It is mostly
misclassified as disorder but it is basically a difficulty. Many research’s in detecting
dyslexic or non-dyslexic patients in many different ways were carried out by
researchers, however the focus of our current study is to classify the dyslexia and
further do the subgroup analysis of dyslexic patients using machine learning algorithms.
Machine learning is a technique used in computer science in which models
automatically learns the way humans learn. They learn from the data given to them.
Machine learning algorithms are used mostly for prediction. In the current scope of
research, for this purpose Different assessment techniques are used to assess students
based on skills like reading, writing and sounds. The score of these assessments are
further used for classification and analysis of subgroups in dyslexic patients. Supervised
and un-supervised both techniques are used for classification and subgroup
analysis. For classification purpose both binary class and multiclass classification
is done. For binary classification purpose neural network, for multiclass classification
svm and for clustering subgroup analysis k-means algorithm is used. In binary
classification dyslexic and non- dyslexic patients have been classified. In multiclass
classification different classes are classified and after this clustering is done using
k-means algorithm to make different groups of different dyslexic level.
