Prediction of Agoraphobia Disease Based on Machine Learning Techniques

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.