Patient Benefactor Linker

Abstract

This paper discusses a new evolution in the healthcare sector through a device by investigatingthe principle application of Artificial Neural Networks (ANN) for the selection of an optimalbenefactor-donor match in organ transplantation. The device aims to correlate ABO blood type,age and bone density of healthy subjects. Firstly, linker phase integrates a light intensity(lux)meter and an RGB Color Sensor module to perform an experimental observation of agglutinationof RBC's which is measured through a halogen illumination source that measures the lightintensity which is displayed on a screen through the microprocessor interface. Secondly, weaim to study the possibility of calcium quantification via near-infrared spectroscopy to estimatebone density which involves the use of an emitting source and a photodiode as a detector/receiver. At last the device involves designing an Artificial Neural Network (ANN) model throughthe Neural Network Toolbox of MATLAB software to get the optimal network architecturesuitable for the analysis. This architecture is achieved by simulating different Artificial NeuralNetwork (ANN) configurations. We used a non-linear ANN which can predict benefactor andpatient organ matches, while measuring ABO blood typing and calcium density of the donorsin real time and for recognizing mapping functions for which there is no requirement for aparticular basis of functions. A database was created through an intensive survey of benefactorprofiles. The results generated by ANN are promising for identifying optimal benefactor andpatient matches. This approach has potential benefits as an increase in the number of input andparameters will provide better matches and risk associated with human error are reduced. Thenetwork can further be modelled to predict survival rates.