Data Wrangling and Data Leakage in Machine Learning for Healthcare
N. Saravanan, Goski Sathish, J. M Balajee · SSRN Electronic Journal · 2018
Nowadays, healthcare and life sciences overall have produced massive amounts of real-time data by enterprise resource planning (ERP). These large of amount data is a difficult task to handle, and intimidation of data leakage by inside worker rises, the firms are smearing way out for security such as Data Loss Prevention (DLP) and Digital Rights Management (DRM) to prevent data leakage. On the other hand, data leakage system also turns into varied and challenging to avert data leakage. Machine learning techniques are used for the handling of significant data by evolving algorithms and set of rules to provide the prerequisite results to the workers. Deep learning has automatic feature extraction that grasps the essential features necessary for the solution of the problem. It reduces the issue of the workers to select elements explicitly to solve the problems for supervised, unsupervised and semi-supervised for healthcare data’s.