SHAP Algorithm for Healthcare Data Classification

Samson Mihirette, Qing Tan

Research output: Chapter in Book/Report/Conference proceedingPublished Conference contributionpeer-review


To strengthen the healthcare data privacy protecting techniques and ensure the transparency of healthcare data exchange, many data privacy-preserving methods have been introduced. This paper highlights privacy concerns and introduces techniques and research directions towards data privacy in Healthcare Information Systems (HIS). The paper demonstrates the use and the power of the Shapley Additive exPlanations (SHAP) algorithm to identify and classify critical data elements that can put personal privacy at risk within a dataset. A conceptual patient-centric healthcare information system architecture with a data broker is proposed in this paper. The proposed architecture also includes the privacy broker that leverages application programming interface services and integration middleware in safeguarding healthcare data privacy.

Original languageEnglish
Title of host publicationHybrid Artificial Intelligent Systems - 17th International Conference, HAIS 2022, Proceedings
EditorsPablo García Bringas, Hilde Pérez García, Francisco Javier Martínez de Pisón, José Ramón Villar Flecha, Alicia Troncoso Lora, Enrique A. de la Cal, Alvaro Herrero, Francisco Martínez Álvarez, Giuseppe Psaila, Hector Quintián, Emilio Corchado
Number of pages12
Publication statusPublished - 2022
Event17th International Conference on Hybrid Artificial Intelligence Systems, HAIS 2022 - Salamancaa, Spain
Duration: 5 Sep. 20227 Sep. 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13469 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference17th International Conference on Hybrid Artificial Intelligence Systems, HAIS 2022


  • Data privacy
  • Data privacy broker
  • Healthcare Information System (HIS)
  • SHAP


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