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

Computers, Materials & Continua 2019年第3期58卷 585-601页

作者： alia alabdulkarim Mznah Al-Rodhaan Yuan Tian Abdullah Al-Dhelaan Information Technology Department King Saud UniversityKingdom of Saudi Arabia Computer Science Department King Saud UniversityKingdom of Saudi Arabia Nanjing Institute of Technology China.

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 *** systems are used to improve physicians’diagnostic processes in terms of speed and *** data-mining techniques,a clinical decision support system builds a classification model from hospital’s dataset for diagnosing new patients using their *** 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 *** 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 ***,our model is validated against the privacy requirements of the hospitals’datasets and votes,and patients’diagnosed symptoms.

关键词： Privacy-preserving clinical decision-support system random forests.

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Privacy-Preserving Healthcare System for Clinical Decision-Support and Emergency Call Systems

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Communications and Network 2017年第4期9卷 249-274页

作者： alia alabdulkarim Mznah Al-Rodhaan Yuan Tian Information Technology Department King Saud University Riyadh Kingdom of Saudi Arabia Computer Science Department King Saud University Riyadh Kingdom of Saudi Arabia

Healthcare centers always aim to deliver the best quality healthcare services to patients and earn their satisfaction. Technology has played a major role in achieving these goals, such as clinical decision-support systems and mobile health social networks. These systems have improved the quality of care services by speeding-up the diagnosis process with accuracy, and allowing caregivers to monitor patients remotely through the use of WBS, respectively. However, these systems’ accuracy and efficiency are dependent on patients’ health information, which must be inevitably shared over the network, thus exposing them to cyber-attacks. Therefore, privacy-preserving services are ought to be employed to protect patients’ privacy. In this work, we proposed a privacy-preserving healthcare system, which is composed of two subsystems. The first is a privacy-preserving clinical decision-support system. The second subsystem is a privacy-preserving Mobile Health Social Network (MHSN). The former was based on decision tree classifier that is used to diagnose patients with new symptoms without disclosing patients’ records. Whereas the latter would allow physicians to monitor patients’ current condition remotely through WBS;thus sending help immediately in case of a distress situation detected. The social network, which connects patients of similar symptoms together, would also provide the service of seeking help of near-by passing people while the patient is waiting for an ambulance to arrive. Our model is expected to improve healthcare services while protecting patients’ privacy.

关键词： Privacy-Preserving CDSS MHSN Decision Tree Random Forest Random Decision Tree Opportunistic Computing

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