Abstract 550P
Background
Breast cancer is the most commonly diagnosed cancer among women worldwide. Breast cancer patients experience significant distress relating to their diagnosis and treatment. Managing this distress is critical for improving the lifespan and quality of life of breast cancer survivors. In recent years, the integration of artificial intelligence into medical technologies has enabled the analysis and prediction of disease risk factors, as well as research on disease diagnosis and mortality. Machine learning algorithms are particularly useful for effectively extracting and analyzing large volumes of data in exploratory research. This study aimed to assess the level of distress in breast cancer survivors and analyze the variables that significantly affect distress using machine learning techniques.
Methods
This cross-sectional survey study aimed to assess the level of distress and determine the factors influencing distress in breast cancer patients who have completed primary treatment for breast cancer. A survey was conducted with 641 adult breast cancer patients using the National Comprehensive Cancer Network Distress Thermometer tool. Participants identified various factors that caused distress. Training and testing of the machine learning models were performed and the feature importance was verified and visualized using Python version 3.9.16.
Results
The survey results indicated that 57.7% of the participants experienced severe distress. The top-three best-performing models indicated that depression, dealing with a partner, housing, work/school, and fatigue are the primary indicators. Among the emotional problems, depression, fear, worry, loss of interest in regular activities, and nervousness were determined as significant predictive factors.
Conclusions
Therefore, machine learning models can be effectively applied to determine various factors influencing distress in breast cancer patients who have completed primary treatment, thereby identifying breast cancer patients who are vulnerable to distress in clinical settings.
Clinical trial identification
Editorial acknowledgement
Legal entity responsible for the study
The authors.
Funding
Has not received any funding.
Disclosure
All authors have declared no conflicts of interest.