Depression Detection By SVM

Computer Science Engineering

Depression is a big concern nowadays. Our project uses technology to spot signs of depression in text messages. We clean up the text by doing things like removing unnecessary words and breaking words down to their simplest form. Our approach involves analysing the language used in messages to detect common patterns and expressions associated with depression. We picked a specific method called SVM to help us figure out if a text message shows signs of depression. The project involves several stages, starting with data collection from diverse sources such as clinical records, online platforms, and wearable devices. Preprocessing techniques are applied to clean and prepare the data for analysis, including feature selection and extraction to enhance model performance. A Support Vector Machine (SVM) classifier is trained using the processed data. SVM is chosen for its ability to handle high-dimensional data, nonlinear relationships, and potential class imbalances commonly found in mental health datasets. The model is optimized through hyperparameter tuning and cross- validation to improve its generalization and predictive capabilities. The results and findings of this research contribute to the advancement of automated systems for depression screening and risk assessment. Such systems have the potential to assist healthcare professionals in early intervention and personalized treatment planning, ultimately improving outcomes for individuals affected by depression.

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