Abstract:
The objective of this research is to tackle urgent problems in the early identification of
Anxiety Disorder, specifically in Bangladesh. The country is renowned for its high
prevalence of this mental disorder, which is often overlooked. The study aims to predict
disorders using machine learning techniques, specifically targeting young adults. It’s a
mental health condition characterized by nervousness, panic, fear, sweating, and rapid
heartbeat. The study uses machine learning models to predict anxiety disorders in
individuals of all ages. The study employs various algorithms like CatBoost, Random
Forest, SVM, Bagging, XGBoost, Logistic Regression, Decision Tree, and Naive Bayes
to analyze data from surveys with a focus on university students. The data was processed
using Python and Pandas library, with preprocessing performed, including handling null
values and manual review. The survey results show a strong correlation between
symptoms and anxiety disorder, indicating that machine learning can effectively identify
potential anxiety cases. The anxiety dataset was analyzed using various ML algorithms,
with CatBoost achieving the highest accuracy of 92.81%, followed by RF, SVM,
Bagging, LR, XGBoost, DT, and GNB. The approach is revolutionary in Bangladesh,
where data-driven healthcare methods are rare. The study's findings are relevant in both
academic and practical circles since they provide a more cost-effective and efficient
technique of mental diagnosis than previous approaches. Completing the dataset, refining
the models to accommodate a wider range of demographics, and incorporating these
results into public health initiatives are possible future directions.
