Author:
Chen Jing,Liu Aijun,Zhang Hongjun,Yang Shengyi,Zheng Hui,Zhou Ning,Li Peng
Abstract
AbstractWith the rapid development of AI and big data mining technologies, computerized medical decision-making has become increasingly prominent. The aim of high-utility pattern mining (HUPM) is to discover meaningful patterns in medical databases that contribute to maximizing the utility from the perspective of diagnosis. However, HUPM pays less attention to the interpretability and explainability of these patterns in medical decision-making scenarios. This paper proposes a novel algorithm called the Improved fuzzy high-utility pattern mining (IF-HUPM) to address this problem. First, the paper applies a fuzzy preprocessing method to divide the fuzzy intervals of a medical quantitative data set, which enhances the fuzziness and interpretability of the data. Next, in the process of IF-HUPM, both fuzzy tree and list structures are employed to calculate fuzzy high-utility values. By combining the characteristics of the one-stage and two-stage algorithms of HUPM, an adaptive-phase Fuzzy HUPM hybrid frame is proposed. The experimental results demonstrate that the proposed IF-HUPM algorithm enhances both accuracy and efficiency and the mining process requires less time and space on average.
Funder
Natural Science Foundation of Inner Mongolia Autonomous Region of China
Scientific Research Project of Baotou Teachers' College
Natural Science Research Project of Department of Education of Guizhou Province
The subject is sponsored by the National Natural Science Foundation of P. R. China
Publisher
Springer Science and Business Media LLC
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