Individualized Quality-of-Life Monitoring as an Alternative to Population-Based Norms in Preventive Medicine

This study proposes an individualized approach to quality-of-life monitoring that shifts from population-based reference norms to personalized physiological comfort zones. By modelling each participant?s baseline across eight biometric, psychophysiological, and environmental parameters, the method detects deviations that traditional standardized thresholds may overlook. Artificial data were generated to simulate diverse human profiles, followed by anomaly detection using personal comfort zones defined as ± 2 standard deviations. Principal Component Analysis was applied to visualize multidimensional health trajectories and highlight temporal instability. The results demonstrate a reduction in false positive and false negative compared to population norms and support the use of individualized baselines as a more sensitive, personalized alternative for preventive health monitoring.