ENOL: A Robust Learning Based Methodology to Predict Mental Health Illness by Using Elevated Neural Optimization Logic

The necessity for smart predictive tools to facilitate early diagnosis and intervention is emphasized by the rising incidence of mental health issues. This paper introduces Elevated Neural Optimization Logic (ENOL), that is intended to predict mental health illness by utilizing advanced neural network optimization techniques. To measure the efficiency of the proposed scheme, it is cross-validated with the conventional known as K-Nearest Neighbor (KNN). ENOL, to analyze complex patterns in mental health data, employs a deep learning framework optimized with complex optimization algorithms. ENOL employs deep architectures that improve optimization approaches, depending on the number of inputs among other things, to fine-tune the mental health condition by improving both precision and effectiveness in forecasting. The ENOL strategy predictive performance was significantly enhanced through experimental validation on a variety of datasets that encompassed a variety of mental health conditions. The effectiveness and newness as well were successful even for the model with 98% accuracy in the prediction. This level of accuracy shows the model is capable of accurately identifying prospective mental health problems and distinguishing between various conditions. The findings emphasize the potential level of ENOL is considered as an important tool for early mental health diagnosis, providing a data-driven strategy to comprehending and dealing with mental health challenges.