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Early disease detection and prevention methods based on effectiveinterventions are gaining attention. Machine learning technology has enabledprecise disease prediction by capturing individual differences in multivariatedata. Progress in precision medicine has revealed that substantialheterogeneity exists in health data at the individual level and that complexhealth factors are involved in the development of chronic diseases. However, itremains a challenge to identify individual physiological state changes incross-disease onset processes because of the complex relationships amongmultiple biomarkers. Here, we present the health-disease phase diagram (HDPD),which represents a personal health state by visualizing the boundary values ofmultiple biomarkers that fluctuate early in the disease progression process. InHDPDs, future onset predictions are represented by perturbing multiplebiomarker values while accounting for dependencies among variables. Weconstructed HDPDs for 11 non-communicable diseases (NCDs) from a longitudinalhealth checkup cohort of 3,238 individuals, comprising 3,215 measurement itemsand genetic data. Improvement of biomarker values to the non-onset region inHDPD significantly prevented future disease onset in 7 out of 11 NCDs. Ourresults demonstrate that HDPDs can represent individual physiological states inthe onset process and be used as intervention goals for disease prevention.

Individual health-disease phase diagrams for disease prevention based on machine learning