Meta Emotional Intelligence for Early Bone Abnormality Detection

This research addresses the imperative for early detection of bone abnormalities through the innovative integration of meta-emotional intelligence. Meta-emotional intelligence, a high level of emotional understanding encompassing both personal and others' emotions, is explored for its potential link to bone health. Current evidence suggests a correlation between meta-emotional intelligence and bone diseases, emphasizing the need for advanced technologies in early detection. Approximately 1.5 million individuals suffer from bone diseases, making early detection crucial. Existing methods for bone abnormality detection include neuro-cognitive models, deep learning frameworks, and sentiment analysis, each with limitations. To overcome these, a novel approach is proposed, combining image and text classification models. The image classification model employs a Convolutional Neural Network on X-ray images, achieving an 86% accuracy rate. Simultaneously, a text classification model analyzes emotional content to predict bone abnormalities with promising results. The fusion of both models presents a holistic approach, enhancing diagnostic accuracy and contributing to proactive healthcare interventions. The integration of meta-emotional intelligence provides a unique perspective, bridging emotional expressions and physiological conditions for early intervention, personalized healthcare, and improved patient outcomes