AUTHOR=Mantey Eric Appiah , Zhou Conghua , Anajemba Joseph Henry , Okpalaoguchi Izuchukwu M. , Chiadika Onyeachonam Dominic-Mario 

TITLE=Blockchain-Secured Recommender System for Special Need Patients Using Deep Learning

JOURNAL=Frontiers in Public Health

VOLUME=Volume 9 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2021.737269

DOI=10.3389/fpubh.2021.737269

ISSN=2296-2565

ABSTRACT=Recommender systems offer several advantages to hospital data management units and patients with special needs. These systems are more dependent on the extreme subtle hospital-patient data. Thus, disregarding patients with special needs’ confidentiality is not an option. In recent times, several proposed techniques failed to cryptographically guarantee the patients special needs data privacy in the diet recommender systems (RSs) deployment. Therefore, in order to tackle this pitfall, this paper incorporates a blockchain privacy system (BPS) into deep learning for diet recommendation system for patients with special needs. Our proposed technique allows patients to get notifications about recommended treatments and medications based on their personalized data without revealing their confidential information. Additionally, the paper implemented machine and deep learning algorithms like RNN, Logistic Regression, MLP etc on an Internet of Medical Things (IoMT) dataset acquired via the internet and hospitals that comprises 50 patients’ data with 13 features of various diseases and 800 products. The product section has a set of eight features. The IoMT data features were analysed with BPS and further encoded prior to the application of deep and machine learning-based frameworks. The performance of different machine and deep learning methods were carried out and the results verify that LSTM technique is more effective than other schemes as regarding prediction accuracy, precision, F1-measures and recall in a secured blockchain privacy system. We attained 97.74% accuracy utilizing the LSTM deep learning model. Also, precision of 98%, recall and F1-measure of 99% each for the allowed class was attained. For the disallowed class, the scores were 89%, 73% and 80% for precision, recall and F1 Measure respectively. The performance of our proposed BPS is subdivided in two categories: the secured communication channel of the recommendation system and an enhanced deep learning approach using health base medical dataset that spontaneously identifies what food a patient with special needs should have based on their disease and certain features including gender, weight, age etc. The proposed system is outstanding as none of the earlier revised literatures described a recommender system of this kind.