Classification based on invisible features and thereby finding the effect of tuberculosis vaccine on COVID-19

TL;DR

This paper introduces a neural network-based method to classify data based on invisible features, demonstrating its effectiveness in predicting COVID-19 metrics and classifying German districts by vaccination history, revealing potential vaccine effects.

Contribution

The work presents a novel neural network approach using logcosh loss to identify branches of set-valued functions and classify data based on invisible features like vaccination status.

Findings

Successfully classified COVID-19 data and German districts using invisible features.

Predicted COVID-19 cases and related metrics with high accuracy.

Classified East and West German districts based on vaccination history.

Abstract

In the case of clustered data, an artificial neural network with logcosh loss function learns the bigger cluster rather than the mean of the two. Even more so, the ANN when used for regression of a set-valued function, will learn a value close to one of the choices, in other words, it learns one branch of the set-valued function with high accuracy. This work suggests a method that uses artificial neural networks with logcosh loss to find the branches of set-valued mappings in parameter-outcome sample sets and classifies the samples according to those branches. The method not only classifies the data based on these branches but also provides an accurate prediction for the majority cluster. The method successfully classifies the data based on an invisible feature. A neural network was successfully established to predict the total number of cases, the logarithmic total number of cases, deaths, active cases and other relevant data of the coronavirus for each German district from a number of input variables. As it has been speculated that the Tuberculosis vaccine provides protection against the virus and since East Germany was vaccinated before reunification, an attempt was made to classify the Eastern and Western German districts by considering the vaccine information as an invisible feature.