Outsourced Privacy-Preserving Random Decision Tree Algorithm Under Multiple Parties for Sensor-Cloud Integration

2017 ◽  
pp. 525-538
Author(s):  
Ye Li ◽  
Zoe L. Jiang ◽  
Xuan Wang ◽  
S. M. Yiu ◽  
Junbin Fang
Keyword(s):  
Decision Tree ◽  
Privacy Preserving ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Sensor Cloud

Outsourced privacy-preserving C4.5 decision tree algorithm over horizontally and vertically partitioned dataset among multiple parties

2017 ◽  
Vol 22 (S1) ◽  
pp. 1581-1593 ◽  
Author(s):  
Ye Li ◽  
Zoe L. Jiang ◽  
Lin Yao ◽  
Xuan Wang ◽  
S. M. Yiu ◽  
...  
Keyword(s):  
Decision Tree ◽  
Privacy Preserving ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
C4.5 Decision Tree

scholarly journals PPSDT: A Novel Privacy-Preserving Single Decision Tree Algorithm for Clinical Decision-Support Systems Using IoT Devices

Sensors ◽  
2019 ◽  
Vol 19 (1) ◽  
pp. 142 ◽  
Author(s):  
Alia Alabdulkarim ◽  
Mznah Al-Rodhaan ◽  
Tinghuai Ma ◽  
Yuan Tian
Keyword(s):  
Decision Support ◽  
Decision Tree ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Privacy Preserving ◽  
Decision Tree Algorithm ◽  
Tree Algorithm

Medical service providers offer their patients high quality services in return for their trust and satisfaction. The Internet of Things (IoT) in healthcare provides different solutions to enhance the patient-physician experience. Clinical Decision-Support Systems are used to improve the quality of health services by increasing the diagnosis pace and accuracy. Based on data mining techniques and historical medical records, a classification model is built to classify patients’ symptoms. In this paper, we propose a privacy-preserving clinical decision-support system based on our novel privacy-preserving single decision tree algorithm for diagnosing new symptoms without exposing patients’ data to different network attacks. A homomorphic encryption cipher is used to protect users’ data. In addition, the algorithm uses nonces to avoid one party from decrypting other parties’ data since they all will be using the same key pair. Our simulation results have shown that our novel algorithm have outperformed the Naïve Bayes algorithm by 46.46%; in addition to the effects of the key value and size on the run time. Furthermore, our model is validated by proves, which meet the privacy requirements of the hospitals’ datasets, frequency of attribute values, and diagnosed symptoms.

Visual Outcome and Development of a Decision Tree Algorithm for Predicting Systemic Diseases Associated with Uveitis

2018 ◽  
Author(s):  
Peizeng Yang ◽  
Zhenyu Zhong ◽  
Liping Du ◽  
Fuzhen Li ◽  
Hong Li ◽  
...  
Keyword(s):  
Decision Tree ◽  
Visual Outcome ◽  
Systemic Diseases ◽  
Decision Tree Algorithm ◽  
Tree Algorithm

scholarly journals Corrigendum to “Pediatric SMA patients with complex spinal anatomy: Implementation and evaluation of a decision-tree algorithm for administration of nusinersen”

2021 ◽  
Author(s):  
Laura Carrera-García ◽  
Jordi Muchart ◽  
Juan José Lazaro ◽  
Jessica Expósito-Escudero ◽  
Daniel Cuadras ◽  
...  
Keyword(s):  
Decision Tree ◽  
Decision Tree Algorithm ◽  
Tree Algorithm

scholarly journals Clasiffication of Heart Disease using Decision Tree Algorithm

2020 ◽  
Vol 1003 ◽  
pp. 012119
Author(s):  
A Ishak ◽  
A Ginting ◽  
K Siregar ◽  
C Junika
Keyword(s):  
Heart Disease ◽  
Decision Tree ◽  
Decision Tree Algorithm ◽  
Tree Algorithm
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