This paper is published in Volume-4, Issue-1, 2018
Area
Data Analytics
Author
Shreyal Gajare
Co-authors
Shilpa Sonawani
Org/Univ
Maharashtra Institute of Technology, Pune, Maharashtra, India
Pub. Date
05 January, 2018
Paper ID
V4I1-1159
Publisher
Keywords
Electronic Health Record (EHR), Feature Selection, Regression technique, Deep Neural Network (DNN)

Citationsacebook

IEEE
Shreyal Gajare, Shilpa Sonawani. Automatic Feature Selection from EHR & DNN Modeling, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Shreyal Gajare, Shilpa Sonawani (2018). Automatic Feature Selection from EHR & DNN Modeling. International Journal of Advance Research, Ideas and Innovations in Technology, 4(1) www.IJARIIT.com.

MLA
Shreyal Gajare, Shilpa Sonawani. "Automatic Feature Selection from EHR & DNN Modeling." International Journal of Advance Research, Ideas and Innovations in Technology 4.1 (2018). www.IJARIIT.com.

Abstract

Recently there are a lot of advancements in healthcare technology. Amongst, Electronic Health Record (EHR) is an upcoming trend which stores patients’ demographics, lab tests & results, medical history, habits etc. collaborated in electronic form. EHR is huge data, which is difficult to maintain and retrieve. So the idea of health risk prediction is formulated in this work. To get the relevant data from EHR, feature selection technique is used. Feature selection is responsible to collect only important and needed data from the dataset. For feature selection regression method is used in which loss function is proposed due to which accuracy and performance of the model are increased. Further risk prediction is done using neural network model. Deep Neural Network (DNN) is best suited for pattern learning and prediction purpose. It consists of various layers which have their specific function. DNN uses transfer learning to avoid repeated training for the whole system. Dataset considered here is of hypertension. EHR data is also synthetically created for analysis.
Paper PDF