  Early disease detection and prevention methods based on effective
interventions are gaining attention. Machine learning technology has enabled
precise disease prediction by capturing individual differences in multivariate
data. Progress in precision medicine has revealed that substantial
heterogeneity exists in health data at the individual level and that complex
health factors are involved in the development of chronic diseases. However, it
remains a challenge to identify individual physiological state changes in
cross-disease onset processes because of the complex relationships among
multiple biomarkers. Here, we present the health-disease phase diagram (HDPD),
which represents a personal health state by visualizing the boundary values of
multiple biomarkers that fluctuate early in the disease progression process. In
HDPDs, future onset predictions are represented by perturbing multiple
biomarker values while accounting for dependencies among variables. We
constructed HDPDs for 11 non-communicable diseases (NCDs) from a longitudinal
health checkup cohort of 3,238 individuals, comprising 3,215 measurement items
and genetic data. Improvement of biomarker values to the non-onset region in
HDPD significantly prevented future disease onset in 7 out of 11 NCDs. Our
results demonstrate that HDPDs can represent individual physiological states in
the onset process and be used as intervention goals for disease prevention.
