A Parliamentary Framework for Missing Data Imputation using LSTM Model
Abstract
Healthcare prediction system plays main role in offering better health care services.it enable the decision maker of health institutions to take the proper decisions in proper time. since the rapid increasing of electronic health records(HER), it become more challenge to analysis and get advantages of such data. So enhancing quality of training data is a common condition that has considerable impact on the performance of health care prediction system. However, using incomplete dataset to train these systems leads to low accurate results and makes the prediction process more complex. This paper proposes a conceptual model for missing data imputation using LSTM model. This model extends an existed prediction model and show how LSTM model is integrated with other layers of health care prediction system
Published
Issue
Section
Submission of an original manuscript to the Journal of Computing Technologies and Creative Content (JTeC) will be taken to mean that it represents original work not previoussly published, that it is not being considered elsewhere for publication. All submitted articles that are published by JTeC cannot be published anywhere by the authors unless with the permission by JTeC Editors. JTeC reserves the right to the publications of the articles it published, and reserves the right to reuse the articles elsewhere for academic purposes, while still retaining the names of the original authors with the original articles.
JTeC takes the stance that the publication of scholarly research is meant to disseminate knowledge and in a not-for-profit regime, benefits neither publisher nor author financially. It sees itself as having obligation to its author and to society to make content available online now that the technology allows for such possibility.