Patient Benefactor Linker
Patient Benefactor Linker
Keywords:Artificial Neural Network, RGB color sensor module, infrared spectroscopy, MATLAB Neural Network Toolbox, , Optimal benefactor-donor match
This paper discusses a new evolution in the healthcare sector through a device by investigating
the principle application of Artificial Neural Networks (ANN) for the selection of an optimal
benefactor-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 agglutination
of RBC's which is measured through a halogen illumination source that measures the light
intensity which is displayed on a screen through the microprocessor interface. Secondly, we
aim to study the possibility of calcium quantification via near-infrared spectroscopy to estimate
bone 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 through
the Neural Network Toolbox of MATLAB software to get the optimal network architecture
suitable for the analysis. This architecture is achieved by simulating different Artificial Neural
Network (ANN) configurations. We used a non-linear ANN which can predict benefactor and
patient organ matches, while measuring ABO blood typing and calcium density of the donors
in real time and for recognizing mapping functions for which there is no requirement for a
particular basis of functions. A database was created through an intensive survey of benefactor
profiles. The results generated by ANN are promising for identifying optimal benefactor and
patient matches. This approach has potential benefits as an increase in the number of input and
parameters will provide better matches and risk associated with human error are reduced. The
network can further be modelled to predict survival rates.
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