Dyslexia is a specific learning difficulty that is genetic in origin. It is very important to predict the predisposition to dyslexia at an early age. In this thesis, we show that we can predict dyslexia with a different attributes and machine learning algorithms. Various computer systems could quickly and objectively detect the predisposition to dyslexia at an early age and thus be able to offer children appropriate help at the very beginning of their education when the problems are not yet noticeable. In this thesis, we focus on the identification of children with dyslexia by analyzing data obtained from the transcription of audio recordings of reading aloud. We compare and analyze different algorithms and machine learning methods and give results. In the thesis, we found that we can predict the predisposition to dyslexia well with different machine learning algorithms even with a small number of cases.
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