Cloud-Enabled Predictive Modeling of Mental Health Using Ensemble Machine Learning Models and AES-256 Security

Mental health disorders are becoming a common concern among adults and children alike. With the growth of cloud computing and the rising concerns over data privacy, integrating machine learning (ML) with secure cloud infrastructure for mental health prediction helps in early detection and timely intervention. In this paper, we propose a novel approach of integrating a voting-based ensemble learning with AES-256-encrypted cloud infrastructure on AWS for a secure and scalable mental health prediction. The ensemble model combines five popular machine learning classifiers: Logistic Regression, Naïve Bayes, Support Vector Machine (SVM), XGBoost, and Random Forest. The proposed model was constructed using these classifiers as its base learners. The system was trained on a dataset of 7,731 Reddit posts related to mental health. We have employed AWS S3 storage and AES-256 encryption for data security. To evaluate the effectiveness of the proposed model, we compared its performance against individual classifiers using a range of evaluation metrics, including accuracy, precision, recall, and F1-score. The results demonstrate that the ensemble model consistently outperforms the individual classifiers, achieving superior performance across all metrics. The model's overall generalizability may be limited because the dataset, which consisted of Reddit postings, might not accurately represent the language or tone used on other platforms, such as clinical notes or journals. Computational overhead is another consequence of using an ensemble model. However, the model was able to demonstrate high accuracy, scalability, and security, making it a viable choice for deployment in real-world clinical settings for early mental health screening.