DeepReco: Deep Learning Based Health Recommender System Using Collaborative Filtering

Abstract

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.

Recommendation Systems: Algorithms, Challenges, Metrics, and Business Opportunities

“…They evaluated their proposed model using accuracy, specificity, and sensitivity (i.e., true negative rate). The authors in [69] developed an e-health collaborative-based RS using deep learning. They applied CNN (convolutional neural network) algorithm, and they evaluated their recommender using precision, recall, MAE, and RMSE values.…”

Realizing an Efficient IoMT-Assisted Patient Diet Recommendation System Through Machine Learning Model

“…While a hardware device proposed and implemented by [27] has the capacity to assemble huge quantity of data for processed product and further analysis assimilate them into the cloud base which would eventually benefit users in obtaining diet/food recommendations. Furthermore, several techniques for projecting a social media for healthcare data as a heterogeneous healthcare information network were also proposed by [28]. Their experiment shows operational approaches outperform the approaches that are based on contents for dynamic users.…”

AnkFall—Falls, Falling Risks and Daily-Life Activities Dataset with an Ankle-Placed Accelerometer and Training Using Recurrent Neural Networks

“…Clearly, the application of these technologies in the field of e-Health, both in the analysis of physiological signals and as diagnostic aid systems with medical images, has an enormous impact and helps to significantly reduce the workload of healthcare professionals. Several works related to this area can be found [ 25 , 26 , 27 , 28 ], and currently we can even find some interesting applications related to the detection of COVID-19 [ 29 , 30 ]. Due to the rise of these systems, it is interesting to carry out a preliminary dataset study using DL techniques in order to evaluate its quality.…”