Backpropagation and fuzzy algorithm Modelling to Resolve Blood Supply Chain Issues in the Covid-19 Pandemic

TL;DR

This paper presents a hybrid approach combining backpropagation and fuzzy algorithms to optimize blood donor identification and distribution during the Covid-19 pandemic, addressing shortages and demand uncertainty.

Contribution

It introduces a novel system integrating backpropagation and fuzzy logic for efficient blood donor classification and distribution management during pandemic conditions.

Findings

Improved identification of eligible blood donors.

Enhanced blood distribution efficiency during Covid-19.

Automated database querying for donor selection.

Abstract

Bloodstock shortages and its uncertain demand has become a major problem for all countries worldwide. Therefore, this study aims to provide solution to the issues of blood distribution during the Covid-19 Pandemic at Bengkulu, Indonesia. The Backpropagation algorithm was used to improve the possibility of discovering available and potential donors. Furthermore, the distances, age, and length of donation were measured to obtain the right person to donate blood when it needed. The Backpropagation uses three input layers to classify eligible donors, namely age, body, weight, and bias. In addition, the system through its query automatically counts the variables via the Fuzzy Tahani and simultaneously access the vast database.