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Дата издания
25.02.2025Область исследования
Тип исследования
Аннотация (краткое описание)
Suicide rates are increasing, particularly among adolescents. This constitutes a significant concern for public health globally. A multitude of social and emotional factors complicate the identification of individuals at risk. This study employs sophisticated AI methods to forecast suicide risks among adolescents through machine learning techniques. We will implement multiple measures to ensure success. Initially, we cleanse the data and extract essential features. We employ techniques such as TF-IDF and BOW for this purpose. Subsequently, we implemented other machine learning models, including Random Forest, Support Vector Machine (SVM), and XGBoost. We additionally employ deep learning models, such as LSTM and CNN. We evaluated our models using actual data and implemented all components in Python. The findings indicate that our method is proficient at identifying individuals who may be at elevated risk. Our method demonstrates superior risk assessment and preventive strategies compared to existing models. We examine variables such as accuracy, precision, and reliability to substantiate this claim.DOI
10.1109/SATC65530.2025.11136830Тип публикаций
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Scopus