A Smart Intelligent Internet of Things Framework for Predicting Mental Health

Abstract

Crohn's disease is a chronic inflammatory condition affecting the gastrointestinal tract, and it presents significant challenges in both diagnosis and management due to its complex and multifactorial causes. Early identification of Crohn's disease is crucial, as it can lead to timely interventions, improve patient outcomes, and reduce healthcare costs. This study aims to develop predictive models using advanced analytics, machine learning (ML), and clinical data to identify individuals at risk for Crohn's disease.The proposed framework integrates a variety of data sources, including genetic predispositions, microbiome profiles, environmental factors, and clinical markers such as inflammatory biomarkers, endoscopic findings, and patient‐reported symptoms. We employed supervised ML algorithms, including random forests, support vector machines (SVM), and deep learning models, to analyze retrospective datasets from multiple cohorts.The results indicate that these predictive models can achieve high sensitivity and specificity in identifying early‐onset Crohn's disease. Key predictors identified include specific genetic mutations (e.g., NOD2/CARD15), imbalances in gut microbiota, and elevated levels of C‐reactive protein (CRP).A pair of knee x‐ray pictures obtained from the Kaggle website are utilised to assess the proposed ZMPPM. Finally, the performance is evaluated based on metrics like accuracy, F1‐score, recall, precision, specificity and error rate. The model attained an excellent accuracy of 99% with relatively minor error.