Threats and Protections for Machine Learning In Healthcare Systematic Intoxication

Abstract

Machine learning is being used to uncover patterns in massive datasets across a wide range of application domains Machine learning findings are increasingly driving crucial decisions in healthcare and biomedical applications Because healthrelated apps are frequently sensitive any security breach would be disastrous The accuracy of the findings produced by machine learning is of course critical According to recent research some machinelearning algorithms can be hacked by supplementing their training datasets with harmful material resulting in a new type of assault known as poisoning attacks A delay in receiving a diagnosis could be lifethreatening and generate distrust On the other hand a false positive classification may not only cause users to doubt the machinelearning algorithm and potentially quit the entire system but it may also cause patient suffering The suggested attack process provides input data that when added to the training set can either cause targeted errors These attacks can be used on both static and dynamic datasets Data mining was the most efficient one for identifying undiscovered positive regularities that could aid an organizations efficiency For many firms data mining abilities are becoming increasingly vital Data mining assists in the discovery of previously undiscovered and very profitable data in large amounts of data Database learning explorations main goal was to find new patterns in a large amount of data The emergence of inherent design modifications for Electronic Health Records EHRs has typically been hampered by a longstanding emphasis on compliance As customization and data science encourage individuals to participate in the details of their medical and reclaim control over their health information we are in desperate need of such technology We suggest MedRec a revolutionary decentralized record management system that uses blockchain technology to manage EHRs in this study