Abstract
Healthcare is one of the core areas of the medical field in today's digital world. By analyzing large amounts of patient data, healthcare systems are needed to gain insights and help disease prediction. The truth discovery of the Health Recommendation System (TDHRS) is becoming an important platform for medical services. The proposed use of Boltzmann Machine (RBM)-FP-Growth Health Pattern Recognition (RBMG) smart HRS can provide insights into how big data analysis can be used to implement an effective health recommendation engine, and will be used in Transition from a standard solution to a more personalized paradigm in the telemedicine environment. By considering the Root Mean Square Error (RSME) and Mean Absolute Error (MAE) values, the proposed deep learning method (RBM-FP-Growth) exhibits fewer errors than other methods.
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This article (and all articles in the proceedings volume relating to the same conference) has been retracted by IOP Publishing following an extensive investigation in line with the COPE guidelines. This investigation has uncovered evidence of systematic manipulation of the publication process and considerable citation manipulation.
IOP Publishing respectfully requests that readers consider all work within this volume potentially unreliable, as the volume has not been through a credible peer review process.
IOP Publishing regrets that our usual quality checks did not identify these issues before publication, and have since put additional measures in place to try to prevent these issues from reoccurring. IOP Publishing wishes to credit anonymous whistleblowers and the Problematic Paper Screener [1] for bringing some of the above issues to our attention, prompting us to investigate further.
[1] Cabanac G, Labbé C and Magazinov A 2021 arXiv:2107.06751v1
Retraction published: 23 February 2022