Stacking Ensemble Technique for Classifying Breast Cancer / 대한의료정보학회지

OBJECTIVES: Breast cancer is the second most common cancer among Korean women. Because breast cancer is strongly associated with negative emotional and physical changes, early detection and treatment of breast cancer are very important. As a supporting tool for classifying breast cancer, we tried to identify the best meta-learner model in a stacking ensemble when the same machine learning models for the base learner and meta-learner are used. METHODS: We used machine learning models, such as the gradient boosted model, distributed random forest, generalized linear model, and deep neural network in a stacking ensemble. These models were used to construct a base learner, and each of them was used as a meta-learner again. Then, we compared the performance of machine learning models in the meta-learner to determine the best meta-learner model in the stacking ensemble. RESULTS: Experimental results showed that using the GBM as a meta-learner led to higher accuracy than that achieved with any other model for breast cancer data and using the GLM as a meta learner led to low root-mean-squared error for both sets of breast cancer data. CONCLUSIONS: We compared the performance of every meta-learner model in a stacking ensemble as a supporting tool for classifying breast cancer. The study showed that using specific models as a metalearner resulted in better performance than single classifiers, and using GBM and GLM as a meta-learner is appropriate as a supporting tool for classifying breast cancer data.

Factors Influencing Health Related Quality of Life in Adult Survivors of Haematopoietic Stem Cell Transplantation

PURPOSE: The aim of the study is to evaluate the health-related quality of life, psychological symptoms, distress, and sense of coherence in adult haematopoietic stem cell transplantation survivors. METHODS: Fifty two survivors completed four questionnaires after the transplantation. The questionnaires were the Functional Assessment of Cancer Therapy-BMT Scale, the National Cancer Center Psychological Symptom Inventory, the Distress Thermometer, and the Sense of Coherence scale. RESULTS: Quality of life was positively correlated with sense of coherence, whereas sense of coherence was negatively correlated with all psychological symptoms and distress. Hierarchical regression analyses revealed that sense of coherence was the only significant predictor of quality of life after controlling for sex and age at transplantation. Model 2 explained 33.2% of the total variance of quality of life. CONCLUSION: Supporting patients towards improving comprehensibility, manageability and meaningfulness, the three components of sense of coherence, may be beneficial and improve outcomes. Individually pre-transplant and post-transplant assessments of sense of coherence may be of clinical importance, in order to identify patients with unmet needs and to provide rolonged support.