Patient Benefactor Linker

Patient Benefactor Linker


  • Hira Zahid Faculty of Engineering Science  Management and Technology , Ziauddin University
  • Sidra Abid Syed Faculty of Engineering Science  Management and Technology , Ziauddin University
  • Marissa Jerome Faculty of Engineering Science  Management and Technology , Ziauddin University
  • Rida Batool Faculty of Engineering Science  Management and Technology , Ziauddin University
  • Sarmad Shams Biomedical Engineering Department,  Sir Syed University of Engineering and Technology 



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 icroprocessor 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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