| Summary: | 碩士 === 高雄醫學大學 === 醫務管理學研究所 === 99 === Purpose:
The studies about quality of life of breast cancer are increased year by year, but there are little studies about long-term quality of life in Taiwan. Therefore, the purpose of this study is to discuss long-term quality of life of breast cancer patient who accepted the surgery (MRM, BCS or MRM+TRAM), to evaluate the changing trend, to compare the accuracy of different prediction models and to evaluate the predicting factors between Artificial Neural Networks (ANN) and Linear Regression Model (LRM). Finally, the study discusses the worldwide situation by paper review.
Method:
In this prospective study, there are 203 breast cancer patients included from two medical centers. All patients completed three questionnaires (QLQ-BR23, QLQ-C30 and SF-36) at preoperative, 6 months, 1yaer, and 2years after surgery. The study collected the detail information by chart review. The information included patient demography, medical condition, and medical care relevant factors. By using Residual Analysis and Global Sensitivity Analysis, the study discusses the difference between ANN and LRM, and conducts the comparison of important predicting factors.
Result:
The result showed the patients become worse on most of the dimensions at 6 months (p<0.05), and only sexual functioning and sexual enjoyment showed significant improvement (p<0.05); the significant improvement on all dimensions at 1 year (p<0.05) and at the threshold on some dimensions at 2 years (P<0.05). In comparison of different predicting models, the result showed that ANN is better than LRM, and the result also showed the different outcomes between two predicting modals. In addition, the previous studies showed inconsistent outcomes on prediction of ANN and LRM.
Conclusion:
ANN or LRM used on the different fields has its own advantage. If the doctors would like to know Health-Related Quality of Life of patients, they could use ANN to be a major method and LRM to be a secondary method. Although ANN could deal with nonlinear data, there are little studies about quality of life prediction by this method. Therefore, researchers could use ANN to do systemic discussion in the future.
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