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Health outcome prediction using multiple perturbations.

Wen-Chung Lee¹

¹Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan.

|January 9, 2020

Summary

This study introduces a new method for health outcome prediction using multiple perturbations. With vast amounts of personal data, this approach theoretically enables near-certain health predictions.

Area of Science:

Health Informatics
Predictive Analytics
Biostatistics

Background:

Accurate health outcome prediction is crucial for public health and medical practice.
Current prediction methods often lack certainty due to limited data and attributes.
Subtle changes (perturbations) in personal attributes may indicate developing health outcomes.

Purpose of the Study:

To propose and investigate a novel method for health outcome prediction called "prediction using multiple perturbations."
To explore the asymptotic properties of this method as the number of attributes approaches infinity.

Main Methods:

The study proposes a "prediction using multiple perturbations" method.
It analyzes the method's theoretical properties as the number of attributes tends to infinity.

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This is a proof-of-concept investigation.

Main Results:

The proposed method can achieve near-certain health outcome prediction.
This requires collecting personal data with billions or trillions of attributes.
Four specific conditions must also be met for this accuracy.

Conclusions:

Theoretically, near-certain health outcome prediction is possible with big data.
Collecting extensive personal attribute data is key to this predictive power.
This approach offers a potential future direction for highly accurate health prognostics.