Abstract:
Nowadays, depression has spread at an alarming rate among students. There are many
reasons for this such as lack of sleep, academic decline, loneliness, limited mental support,
personal, family or financial problems, etc. In this thesis, I used 1977 datasets where there
were 15 universities. Out of which 9 were public and 6 were private. The data was surveyed
using PHQ-9 method. It has 6 levels. My proposed method is ANN- artificial neural
network. Through which I got 97.98% accuracy.my dataset is a tabular form such as mixed
with demographic, academic, phycological. Poor concentration, lack of sleep, and feelings
of low self-worth were more influential predictors of depression than demographic and
academic information. For better understanding, i used explainable ai (LIME), which full
form is Local Interpretable Model Agnostic Explanation. with the help of this method, we
can easily understand the reason of the depression which is causing more effectively. I also
preprocess the dataset for cleaning the data and understanding for human language to
machine language like labeling hot encoding etc. I also used PCA-IG feature selection
method for feature selection and changed the hyperparameter of each model for knowing
which one is more suitable and will give us more accuracy. The results can help shape
targeted mental health programs, guide policy decisions, and inspire future research that
tracks students over time and involves more universities—helping address mental health
needs in settings where resources are limited.
