Machine Learning Framework for Individualised Health Profiling Using Multimodal Physiological and Neurocognitive Biomarkers

Correct classification of individualised health profiles is paramount for the progress of precision health strategies but is difficult for conventional constitution-based systems based on subjective judgments. This work fills this void by suggesting a machine learning paradigm that objectively characterises individual health types based on multimodal physiological and neurocognitive markers. Heart rate variability (HRV), electroencephalography (EEG), gait dynamics, facial thermography, facial imaging, and cognitive-emotional responses in a virtual reality (VR) environment were obtained from 48 subjects. Feature selection, an integration of analysis of variance (ANOVA) and Random Forest-based importance ranking, minimised the dataset from 214 to 69 key variables. A novel meta ensemble model, PRAK-KNN, using multiple machine learning (ML) algorithms, XGBoost and Extra Trees for adaptive neighbor weighting, achieved an accuracy in classification of up to 76% with different feature subsets. In virtual reality-based tests, cognitive load and emotional arousal were key discriminators among individual profiles. This study illustrates an objective, replicable method for constitution-type classification, opening the door to incorporating classical health ideas into contemporary data-driven personalised medicine paradigms.