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

TL;DR

This paper introduces health-disease phase diagrams (HDPDs) that visualize individual biomarker fluctuations to predict and prevent disease onset, leveraging machine learning on longitudinal health data.

Contribution

The study develops a novel visualization method, HDPD, for representing individual health states and predicting disease onset using multivariate biomarker data.

Findings

HDPDs accurately predict disease onset in multiple NCDs.

Biomarker improvements in HDPD regions significantly reduce disease risk.

HDPDs serve as personalized intervention targets for disease prevention.

Abstract

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.