A Review on Various Machine Learning Approaches for Fingerprint Based Health Information Exchange

Abstract: The personal health records (PHR) is created for personal health information and it provides easy access to a wide range patients, consumers, practitioners and healthcare providers. However, improved accessibility of PHR threatening confidentiality, privacy, and personalized health information security. The concept of biometrics is currently used for healthcare provider's technology to prevent unauthorized access to the individual health data. We are implementing a biometric mechanism which will protect your PHR and makes it easier to control access, Safe exchange of health information. In this article, we have looked at different machine learning approaches to exchange patient information and different biometric identification techniques that provide reliable user authentication to ensure that only authorized persons can access patient health data. In addition, biometric systems can also facilitate remote access to healthcare data by using biometric features as an authentication tool. This paper summarizes the studies conducted on fingerprint matching techniques, their recognition methods and performance analysis. 0.1% to 0.01% way FAR(false acceptance rate),FP(finger print) can be classified as spiral, straight ring, arc, tent arch etc. To ensure the performance of finger print recognition, advanced algorithms are required to improve the clarity of the input fingerprint image.

Keywords: Fingerprint (FP), Biometrics, Security and Privacy, Patient health information (PHI), Electronic Health (ehealth).