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A Privacy-Preserving Algorithm for Clinical Decision-Support Systems Using Random Forest

Alia Alabdulkarim1, Mznah Al-Rodhaan2, Yuan Tian*,3, Abdullah Al-Dhelaan2
Information Technology Department, King Saud University, Kingdom of Saudi Arabia.
Computer Science Department, King Saud University, Kingdom of Saudi Arabia.
Nanjing Institute of Technology, China.
* Corresponding Author: Yuan Tian. E-mail: .

Computers, Materials & Continua 2019, 58(3), 585-601. https://doi.org/10.32604/cmc.2019.05637

Abstract

Clinical decision-support systems are technology-based tools that help healthcare providers enhance the quality of their services to satisfy their patients and earn their trust. These systems are used to improve physicians’ diagnostic processes in terms of speed and accuracy. Using data-mining techniques, a clinical decision support system builds a classification model from hospital’s dataset for diagnosing new patients using their symptoms. In this work, we propose a privacy-preserving clinical decision-support system that uses a privacy-preserving random forest algorithm to diagnose new symptoms without disclosing patients’ information and exposing them to cyber and network attacks. Solving the same problem with a different methodology, the simulation results show that the proposed algorithm outperforms previous work by removing unnecessary attributes and avoiding cryptography algorithms. Moreover, our model is validated against the privacy requirements of the hospitals’ datasets and votes, and patients’ diagnosed symptoms.

Keywords

Privacy-preserving, clinical decision-support system, random forests.

Cite This Article

A. Alabdulkarim, M. Al-Rodhaan, Y. Tian and A. Al-Dhelaan, "A privacy-preserving algorithm for clinical decision-support systems using random forest," Computers, Materials & Continua, vol. 58, no.3, pp. 585–601, 2019.

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This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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