Hybrid PCA-Based Machine Learning Models for Predictive Analytics in Urban Health Monitoring Systems

In this paper, a hybrid PCA-based approach is proposed for predicting the respiratory imbalance in an urban health monitoring system. To do this, three models namely Random Forest, XGBoost, and Multi-Layer Perceptron (MLP) were integrated into one system to improve the probability of prediction while maintaining the trade-off with the dimension reduction - a common variable in big data tasks (which, this effort is focused on). Several variables regarding the environment and health of the patients were used to form a dataset that was later transformed with the help of PCA meaning the results would get better because models with better dimensionality tend to perform better. The Hybrid PCA + MLP model produced the best accuracy which 99% and AUC - ROC which 0.90, there were also 99% produced by XGBoost but AUC - ROC was 0.83. Random forest produced “slightly” less accurate predictions of 95% with an AUC - ROC of 0.72 however; this proved the best method in terms of the computational burden since the training time recorded was about 0.55 seconds, which was lower than that of XGBoost 0.45 seconds and MLP 0.94 seconds. These findings show that hybrid PCA based models do not only improve the accuracy of predictions made but also reduce the amount of computational resources needed, which is ideal for tasks that occur in real time whereby smart urban health monitoring is a part of a bigger CPS system.