Fingerprint-Based Blood Group Prediction Using Multilayer Perceptron(MLP)

There is a global demand for a cheap Blood Group measurement solution, which is especially urgent in underdeveloped nations. Image Processing, which is the most often used device in both affluent and resource-constrained locations, would be an appropriate choice for developing this solution. We suggested a new method for determining blood type from fingerprints using the Multilayer Perceptron (MLP) algorithm. Traditional techniques of blood typing frequently need blood samples, which may be intrusive and time-consuming. This paper suggests a non-invasive approach for predicting an individual's blood type using fingerprints, using improvements in machine learning and biometric technologies. The MLP algorithm analyses fingerprint data and extracts significant traits associated with various blood types. Following thorough research and validation, the suggested method displays great accuracy in blood type prediction, making it a potential alternative to traditional blood typing procedures. This novel technique not only improves the effectiveness of blood group identification but also reduces the necessity for intrusive treatments, resulting in better healthcare practices and increased patient comfort.