A medical assistant decision-making method based on interval belief rule base with explainability

Article type: Research Article

Authors: Kong, Lingkai^{a; 1} | Zhao, Boying^{a; 1} | Li, Hongyu^a | He, Wei^{a; *} | Cao, You^b | Zhou, Guohui^a

Affiliations: [a] Harbin Normal University, Harbin, China | [b] High-tech Institute, Qingzhou, China

Correspondence: [*] Corresponding author: Wei He, Harbin Normal University, Harbin 150025, China. E-mail: [email protected].

Note: [1] Lingkai Kong, Boying Zhao contributed equally to this work.

Abstract: Medical assisted decision-making plays a key role in providing accurate and reliable medical advice. But in medical decision-making, various uncertainties are often accompanied. The belief rule base (BRB) has a strong nonlinear modeling capability and can handle uncertainties well. However, BRB suffers from combinatorial explosion and tends to influence explainability during the optimization process. Therefore, an interval belief rule base with explainability (IBRB-e) is explored in this paper. Firstly, pre-processing using extreme gradient boosting (XGBoost) is performed to filter out features with lower importance. Secondly, based on the filtered features, explainability criterion is defined. Thirdly, evidence reasoning (ER) rule is chosen as an inference tool, while projection covariance matrix adaptive evolutionary strategy (P-CMA-ES) algorithm with explainability constraints is chosen as an optimization algorithm. Lastly, the validation of the model is performed through a breast cancer case. The experimental results show that IBRB-e has good explainability while maintaining high accuracy.

Keywords: Belief rule base, decision-making, medical assistant, explainability, evidence reasoning

DOI: 10.3233/IDA-230648

Journal: Intelligent Data Analysis, vol. Pre-press, no. Pre-press, pp. 1-25, 2024