A Decision Tree Model for Breast Reconstruction of Women with Breast Cancer: A Mixed Method Approach

Abstract

The number of breast reconstructions following mastectomy has increased significantly during the last decades, but women are experiencing a number of conflicts with breast reconstruction decisions. The aim of this study was to develop a decision tree model of breast reconstruction and to examine its predictability. Mixed method design using ethnographic decision tree modeling was used. In the qualitative stage, data were collected using individual and focus group interviews and analyzed to construct a decision tree model. In the quantitative stage, the questionnaire was developed questions based on the criteria identified in the qualitative stage. A total of 61 women with breast cancer participated in 2017. Five major criteria: recovery of body image; impact on recurrence; recommendations from others; financial resources; and confirmation by physicians. The model also included nine predictive pathways. It turns out that the model predicted 90% of decisions concerning whether or not to have breast reconstruction. The findings indicate that the five criteria play a key role in decision-making about whether or not to have breast reconstruction. Thus, more comprehensive issues, including these five criteria, need to be integrated into an intervention for women with breast cancer to make their best decision on breast reconstruction.

Comparing Data Mining Algorithms for Breast Cancer Diagnosis

“…In a systematic review by Li et al, the results showed the most frequently used ML methods for BC prediction from 2013 to 2020 were DT classifiers (19 studies, 61.3%) (42). Besides, the study by Park et al showed that the best meaningful results were observed from the DT model with an accuracy of 90% (47). In our research, the DT approach had a high accuracy of about 96%.…”

The (decision) tree of fertility: an innovative decision-making algorithm in assisted reproduction technique

“…This approach, aimed at identifying the strategy most likely to reach a goal, is simple to understand and interpret and could be combined with other decision techniques, such as logistic regression analyses. For these reasons, the decision tree model has been applied in several medicine branches, such as gastroenterology [36,37], breast oncological surgery [38], cardiology [39], orthopedic surgery [40], and even to diagnose SARS-CoV-2 infection [41]. Specifically in the ART setting, the decision tree model has been previously applied mainly in cost-effectiveness analyses, for instance to identify the most cost-effective ovarian stimulation drug for intra-uterine insemination (IUI) [42]; to evaluate the clinical utility for preimplantation genetic assessment for aneuploidy after IVF in the USA [43], and in Germany [44]; to highlight anti-Müllerian hormone (AMH) serum levels as informative for stimulation dose management for optimizing blastocyst development [45]; and to identify the most cost-effective policy in terms of ART success in case of female age below 38 years comparing expectant management, IUI with ovarian stimulation and IVF [46].…”

Improved Machine Learning-Based Predictive Models for Breast Cancer Diagnosis

“…It has been broadly used in the implementation of the prediction system for various diseases, such as heart disease [ 6 ], lung cancer [ 7 ], and thyroid cancer [ 8 ]. DM and ML techniques have been embedded for diagnosing breast cancer with computer-aided systems [ 9 ], and fuzzy-genetics [ 10 ]. The results of these studies successfully classify the features into two types of tumors by the evaluation of classifier and predicting the incoming tumor based on previous data.…”