Identification of dyscalculia using supervised machine learning algorithms

Dyscalculia is a specific learning disability in which a person faces difficulty in comprehending mathematical concepts. People with dyscalculia tend to face constant difficulties in performing mathematical calculations which affects their daily life. Around 3-7% of the population globally are affected with dyscalculia. The process of diagnosis of dyscalculia has always been a tough task. Techniques such as machine learning are used to for prediction in various fields such as healthcare, education, etc. Subsequently those techniques can be used for diagnosis of dyscalculia. The current study aims to develop a diagnosis model using well known supervised machine learning algorithms such Support Vector Machines (SVM), Simple logistic regression, Naive bayes and Random Forest