Abstract:
In today's world, agoraphobia has become a very common disorder. Agoraphobia disease is a
group of mental illnesses marked by intense emotions of fear and anxiety. Majority of people are
unaware of the condition. It is essential to recognize it early on so that doctors can give better
treatment and prevent it from progressing into a significant problem. Recently machine learning
algorithms can be used to assess a patient's history and find abnormalities by simulating human
thinking or drawing logical inferences. This study reviews the basic ideas and applications of
machine learning algorithms in predicting anxiety disorder types. We try to detect agoraphobia in
the primary stage in this research. We primarily used three feature selection strategies, as well as
a variety of classification algorithms, to accomplish this. We use some classification methods
include the Naive Bayes, Random Forest ,Decision Tree, KNN, and Support Vector Machine
(SVM). After testing random forest classification method in achieving higher accuracy with
98.02% accuracy than all other classification methods currently in use.
