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Title

Prediction of Agoraphobia Disease Based on Machine Learning

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Author

Israt Jahan Intia, Khairul Alam, Khondoker Sangida Ferdous, Md. Mehedi Hasan ,

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Email

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Abstract

• Agoraphobia has become a fairly frequent disorder in today's environment. Agoraphobia is a spectrum of mental diseases characterized by strong dread and anxious feelings. The majority of individuals are completely oblivious of the problem. It's critical to detect it early on so that doctors can provide better treatment and avoid it from becoming a major issue. Recently, machine learning algorithms have been employed to analyze patient records in order to spot anomalies by modeling human thought or forming logical inferences. In this study, we strive to detect agoraphobia in the early stages. The basic theories and uses of machine learning algorithms in identifying anxiety kinds are also reviewed in this article. We primarily employed three feature selection techniques as well as a range of classification algorithms. The Naive Bayes, Random Forest, Decision Tree, KNN, and Support Vector Machine(SVM) are some of the categorization algorithms we utilize. After putting the Random Forest classification technique to the test, it was found to have greater accuracy of 98.02 percent than any other classification method that now is used.
  

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Keywords

Machine learning algorithms , Anxiety disorders , Support vector machine classification , Medical services , Forestry , Prediction algorithms , Feature extraction

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Journal or Conference Name

2022 13th International Conference on Computing Communication and Networking Technologies, ICCCNT 2022

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Publication Year

2022

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Indexing

scopus

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