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Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
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PPSDT: A Novel Privacy-Preserving Single Decision Tree Algorithm for Clinical Decision-Support Systems Using IoT

Alia Alabdulkarim1,2, Mznah Al-Rodhaan3, Tinghuai Ma4

  • 1Information Technology Department, King Saud University, Riyadh 11451, Saudi Arabia. aalabdulkarim@ksu.edu.sa.

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This summary is machine-generated.

This study introduces a novel privacy-preserving clinical decision-support system using a unique decision tree algorithm. It enhances diagnostic accuracy and protects patient data from network attacks.

Keywords:
CDSSprivacy-preservingsingle decision trees

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Area of Science:

  • Computer Science
  • Medical Informatics
  • Cryptography

Background:

  • Healthcare quality relies on patient trust and satisfaction.
  • Internet of Things (IoT) in healthcare offers solutions to improve patient-physician interactions.
  • Clinical Decision-Support Systems (CDSS) enhance healthcare quality through faster, more accurate diagnoses.

Purpose of the Study:

  • To propose a privacy-preserving clinical decision-support system.
  • To develop a novel privacy-preserving single decision tree algorithm for symptom diagnosis.
  • To protect sensitive patient data from network attacks.

Main Methods:

  • Utilized data mining techniques and historical medical records.
  • Developed a classification model for patient symptom diagnosis.
  • Implemented a homomorphic encryption cipher and nonces for data protection.

Main Results:

  • The proposed novel algorithm outperformed the Naïve Bayes algorithm by 46.46%.
  • Analyzed the impact of key value and size on algorithm runtime.
  • Validated the model against privacy requirements, data frequency, and symptom diagnosis accuracy.

Conclusions:

  • The developed system effectively diagnoses new symptoms while preserving patient privacy.
  • The novel algorithm offers significant improvements in diagnostic performance compared to existing methods.
  • The system meets essential privacy requirements for hospital datasets.