Health outcome prediction using multiple perturbations

Abstract

Public health workers and medical practitioners are frequently required to make predictions regarding various health outcomes. However, a prediction with nearly 100% certainty is seldom possible.If a person has a health outcome of concern or is in the process of developing the outcome, many attributes of that person may undergo subtle changes - the perturbations. We propose a method, namely "prediction using multiple perturbations" and investigate its asymptotic properties when the number of attributes tends to infinity. This is a proof-of-concept study.The proposed method can predict the health outcome of a person to near certainty if personal data with billions or trillions of attributes can be collected and 4 conditions (described subsequently in this paper) are met.Collecting personal data with billions or trillions of attributes may someday become possible in the current era of big data. If such information can be obtained, theoretically we can predict the health outcome of a person to near certainty.