Data Mining-Based Classification Algorithms for Predicting Mental Health

Data Mining-Based Classification Algorithms for Predicting Mental Health

연구 분야: Databases

학회: International Conference on Advances in Artificial Intelligence and Machine Learning in Big Data Processinging

The rapid and diverse development of society has led to increased pressures on living conditions, education, and employment, resulting in significant psychological challenges. Addressing the importance of improving mental health education for children has become a subject of widespread concern and crucial for society as a whole. This proposal aims to introduce a novel system based on a common optimization algorithm for evaluating mental health intelligence, addressing the limitations of existing procedures, such as low work efficiency and high misjudgment rates. By combining artificial neural network (ANN) algorithms and extended decision trees, a comprehensive optimization method is proposed. The research begins by examining the current state of intelligence measurement in mental health, utilizing data mining to gather information from mental health intelligence tests. The gathered data is then analyzed and categorized using compound learning algorithms. The system’s effectiveness and superiority are assessed through a series of unique simulation experiments. This research not only addresses the shortcomings of current systems but also proposes innovative strategies for mental health intelligence scoring to enhance precision, effectiveness, and other essential characteristics. Ensuring stability and meeting the needs of users are also primary objectives of this system. Overall, this proposal strives to contribute to the advancement of mental health assessment methodologies and education, benefiting individuals and society as a whole.