Abstract (300 words maximum)
Mental health is a major concern globally and identifying individuals who require treatment is crucial. This project uses Deep Learning algorithms, specifically DenseNet based on the Convolutional Neural Network (CNN) algorithm, to predict whether an individual requires treatment or not. The dataset used for this analysis contains demographic information and survey responses from individuals across various countries. The preprocessing involved imputing missing values, encoding categorical variables, and normalizing the data. Exploratory Data Analysis (EDA) and visualization were conducted to understand the dataset better. The DenseNet model achieved an accuracy of 88% on the test set. The results of this project can aid in identifying individuals who may require mental health treatment, enabling early intervention and improved outcomes.
Academic department under which the project should be listed
CCSE - Computer Science
Primary Investigator (PI) Name
Md Abdullah Al Hafiz Khan
Mental Health Survey Analysis & Prediction Using Deep Learning Algorithms
Mental health is a major concern globally and identifying individuals who require treatment is crucial. This project uses Deep Learning algorithms, specifically DenseNet based on the Convolutional Neural Network (CNN) algorithm, to predict whether an individual requires treatment or not. The dataset used for this analysis contains demographic information and survey responses from individuals across various countries. The preprocessing involved imputing missing values, encoding categorical variables, and normalizing the data. Exploratory Data Analysis (EDA) and visualization were conducted to understand the dataset better. The DenseNet model achieved an accuracy of 88% on the test set. The results of this project can aid in identifying individuals who may require mental health treatment, enabling early intervention and improved outcomes.