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

Frontiers in Public Health

Joseph Henry Anajemba¹  Izuchukwu M. Okpalaoguchi²  Onyeachonam Dominic-Mario Chiadika³  Conghua Zhou⁴  Eric Appiah Mantey⁴ 

[1] Department of Communication Engineering, Hohai University, Changzhou, China;Department of Computer Science, Nnamdi Azikiwe University, Awka, Nigeria;Electronic and Computer Engineering, Brunel University, London, United Kingdom;School of Computer Science & Comm. Engineering, Jiangsu University, Zhenjiang, China;

关键词: blockchain privacy system;    deep learning;    machine learning;    IOMT;    artificial intelligence;   

DOI  :  10.3389/fpubh.2021.737269

来源: Frontiers

【 摘 要 】

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 the confidentiality of patients with special needs is not an option. In recent times, several proposed techniques failed to cryptographically guarantee the data privacy of the patients with special needs in the diet recommender systems (RSs) deployment. In order to tackle this pitfall, this paper incorporates a blockchain privacy system (BPS) into deep learning for a 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 such as RNN, Logistic Regression, MLP, etc., on an Internet of Medical Things (IoMT) dataset acquired via the internet and hospitals that comprises the data of 50 patients with 13 features of various diseases and 1,000 products. The product section has a set of eight features. The IoMT data features were analyzed with BPS and further encoded prior to the application of deep and machine learning-based frameworks. The performance of the different machine and deep learning methods were carried out and the results verify that the long short-term memory (LSTM) technique is more effective than other schemes regarding prediction accuracy, precision, F1-measures, and recall in a secured blockchain privacy system. Results showed that 97.74% accuracy utilizing the LSTM deep learning model was attained. The precision of 98%, recall, and F1-measure of 99% each for the allowed class was also 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 into 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 works of literature described a recommender system of this kind.

【 授权许可】

CC BY