Predicting sick building syndrome using machine learning and epidemiological data for Internet of Thing (IoT) Integration

Sick Building Syndrome (SBS) is a widespread occupational health issue traditionally diagnosed through reactive, subjective symptom questionnaires. This study addresses the critical need for real-time, objective mitigation strategies by developing an automated early warning system to predict multi-categorical SBS risk using machine learning. We conducted a cross-sectional epidemiological study in a Malaysian educational institution, securing a unified dataset from 513 respondents across ten classrooms. Real-time physical environmental parameters and aerosol contaminants, specifically Particulate Matter (PM 2.5 ) and Total Volatile Organic Compounds (TVOC), were captured at a seated breathing zone of 110 cm using high-precision occupational hygiene instruments. This environmental data was synchronized with self-reported health outcomes from a revised MM040NA questionnaire. To address the naturally low prevalence of severe symptoms and mitigate overfitting, our predictive modelling pipeline incorporated a 10-fold stratified cross-validation framework alongside the Synthetic Minority Over-sampling Technique (SMOTE) applied exclusively to training folds. We evaluated five classifiers: Artificial Neural Networks (ANN), Random Forests, Support Vector Classifiers (SVC), K-Nearest Neighbours (KNN), and Logistic Regression (LR), optimizing probability thresholds via the F2-score to prioritize recall. The results demonstrate that General SBS symptoms can be predicted with exceptional sensitivity, with ANN, SVC, and LR models achieving recall rates exceeding 0.90. For predicting the aggregate risk of Total SBS, the distance-based KNN algorithm produced the most balanced F1-score (0.419), serving as a theoretical proof-of-concept for risk clustering. Finally, this research confirms the viability of deploying resource-efficient linear or neural network models onto resource-constrained microcontrollers, providing concrete memory and latency benchmarks for a scalable Internet of Things (IoT) architecture designed for proactive indoor environmental health monitoring. Real-time SBS risk forecasting and automated MVAC actuation via edge AI