Decision-making model construction of a pre-clinical Q&A system for breast tumors

Corresponding author: Li Yifan, srelativity@163.com

DOI: 10.12201/bmr.202303.00029

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  Abstract: [Purpose/Significance] To predict the disease risk of potential or existing breast tumor patients using a decision tree classification model to simulate the expert consultation idea. [Methodology/Procedure] A C4.5 classical classification algorithm and a pessimistic pruning method were used to predict the outcome of patient pre-consultation for 177 case data collected from the study. [Results/Conclusions] A C4.5 decision tree with 76 leaf nodes and "whether postoperative chemotherapy or radiotherapy ends in the hospital" as the root node was generated with 95% prediction accuracy and classified into 3 risk levels according to the classification labels.The C4.5 decision tree was constructed based on a single breast tumor disease, and it is a combination of clinical practice and machine learning algorithm to predict the risk of unattended patients.

  Key words: Breast tumor; C4.5 algorithm; decision tree; model construction

  Submit time: 7 April 2023

  Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity.
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  1	2023-03-23	bmr.202303.00029V1	Download

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