AI-Powered Smart Smile: Early Detection of Mental Health Conditions Through Computational Intelligence

The early detection of mental health conditions is crucial for timely intervention and effective treatment. This study presents the development and evaluation of the “Smart Smile” application, an AI-powered tool designed to detect early signs of mental health issues using computational intelligence. The primary objective of this research is to design a robust system that leverages artificial intelligence and machine learning algorithms to analyze user interactions and biometric data to identify potential mental health concerns. The scope of the study includes the integration of various data sources such as facial expressions, voice tone, text input, and user behavior patterns. The methods employed in this research encompass data collection through user surveys and real-time monitoring, followed by the application of machine learning techniques for data analysis. Specifically, convolutional neural networks (CNNs) and natural language processing (NLP) algorithms are utilized to process and interpret the collected data. Key findings from this study indicate that the Smart Smile application can accurately predict early signs of conditions such as depression and anxiety with a high degree of accuracy. The application's performance is validated through extensive testing and cross-validation against established mental health assessment tools. The results demonstrate the potential of AI and computational intelligence in enhancing mental health diagnostics and providing users with actionable insights. In conclusion, the AI-powered Smart Smile application represents a significant advancement in the field of mental health care, offering a scalable and accessible solution for early detection of mental health conditions. Future work will focus on improving the algorithm's accuracy, expanding the dataset, and integrating additional features to support a broader range of mental health conditions.