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09.09.2019 | Methodologies and Application | Ausgabe 10/2020

Soft Computing 10/2020

Hybrid Context Aware Recommendation System for E-Health Care by merkle hash tree from cloud using evolutionary algorithm

Zeitschrift:
Soft Computing > Ausgabe 10/2020
Autoren:
N. Deepa, P. Pandiaraja
Wichtige Hinweise
Communicated by V. Loia.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Abstract

Privacy preservation permits doctors to outsource the huge encrypted reports to the cloud and permits the authenticated patients to have a safe search over the reports without leaking the private information. The doctors in our proposed have used the merkle hash tree for storing the reports of all the patients in the hospital. The existing schemes have used many types of trees like binary tree, red–black tree, spanning tree, B+ tree, etc., for the index generation purpose. Since the security is less and the searching time is high for the above said trees, we have proposed the index generation phase based on the merkle hash tree based on the evolutionary algorithm and it takes less time for searching and highly secure for storing the patient reports. The evolutionary algorithm is used for breeding the new data’s through crossover as well as mutation operations to give confinement to new children. When the patient submits the search request for specialized doctor, based on the patient disease our protocol will recommend the specialized doctors and send the recommended doctors information to the patients who have the highest rating in the online social networks. After receiving the recommended results, the patient can have the treatment via online booking appointment, video call or in person based on the appointment booked. After completely cured, the patients can rate the doctors based on the medicine satisfaction, doctors’ fees and doctor’s response over the call. In this mechanism, we have used the hybrid context aware recommendation system collaborative filtering for rating the doctors based on their performance. After rating the doctors, our protocol has measured the accuracy based on the predicted rating and the true rating. This kind of accuracy metrics is used for ranking the good doctors in the top rank for the patient use. Our proposed work Hybrid Context Aware Recommendation System for E-Health Care (HCARS-EHC) is implemented, and the implementation results of HCARS-EHC illustrate that our protocol is efficient based on the privacy preservation, recommendation and ranking with less computation and communication complexity.

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