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Article

DeepReco: Deep Learning Based Health Recommender System Using Collaborative Filtering

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School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar 751024, India
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School of Computer Application, KIIT Deemed to be University, Bhubaneswar 751024, India
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Center for Robust Speech Systems, The University of Texas at Dallas, Richardson, TX 75080, USA
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Authors to whom correspondence should be addressed.
Received: 15 March 2019 / Revised: 16 May 2019 / Accepted: 18 May 2019 / Published: 22 May 2019
(This article belongs to the Section Computational Engineering)
In today’s digital world healthcare is one core area of the medical domain. A healthcare system is required to analyze a large amount of patient data which helps to derive insights and assist the prediction of diseases. This system should be intelligent in order to predict a health condition by analyzing a patient’s lifestyle, physical health records and social activities. The health recommender system (HRS) is becoming an important platform for healthcare services. In this context, health intelligent systems have become indispensable tools in decision making processes in the healthcare sector. Their main objective is to ensure the availability of the valuable information at the right time by ensuring information quality, trustworthiness, authentication and privacy concerns. As people use social networks to understand their health condition, so the health recommender system is very important to derive outcomes such as recommending diagnoses, health insurance, clinical pathway-based treatment methods and alternative medicines based on the patient’s health profile. Recent research which targets the utilization of large volumes of medical data while combining multimodal data from disparate sources is discussed which reduces the workload and cost in health care. In the healthcare sector, big data analytics using recommender systems have an important role in terms of decision-making processes with respect to a patient’s health. This paper gives a proposed intelligent HRS using Restricted Boltzmann Machine (RBM)-Convolutional Neural Network (CNN) deep learning method, which provides an insight into how big data analytics can be used for the implementation of an effective health recommender engine, and illustrates an opportunity for the health care industry to transition from a traditional scenario to a more personalized paradigm in a tele-health environment. By considering Root Square Mean Error (RSME) and Mean Absolute Error (MAE) values, the proposed deep learning method (RBM-CNN) presents fewer errors compared to other approaches. View Full-Text
Keywords: collaborative filtering; deep learning; CNN; health recommender system; MF; RBM; SVD collaborative filtering; deep learning; CNN; health recommender system; MF; RBM; SVD
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MDPI and ACS Style

Sahoo, A.K.; Pradhan, C.; Barik, R.K.; Dubey, H. DeepReco: Deep Learning Based Health Recommender System Using Collaborative Filtering. Computation 2019, 7, 25. https://0-doi-org.brum.beds.ac.uk/10.3390/computation7020025

AMA Style

Sahoo AK, Pradhan C, Barik RK, Dubey H. DeepReco: Deep Learning Based Health Recommender System Using Collaborative Filtering. Computation. 2019; 7(2):25. https://0-doi-org.brum.beds.ac.uk/10.3390/computation7020025

Chicago/Turabian Style

Sahoo, Abhaya K., Chittaranjan Pradhan, Rabindra K. Barik, and Harishchandra Dubey. 2019. "DeepReco: Deep Learning Based Health Recommender System Using Collaborative Filtering" Computation 7, no. 2: 25. https://0-doi-org.brum.beds.ac.uk/10.3390/computation7020025

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