OBJECTIVE: To introduce the partitioning algorithm of classification tree model, and to explore the value of this data mining technique applied in data analysis of multifactorial diseases as malignant tumors. METHODS: Data was analyzed from a survey that conducted on 84 breast cancer patients and 273 cancer-free controls selected randomly in Jiashan county. The classification tree model was constructed using Exhaustive CHAID method and evaluated by the Risk statistics and the area under the ROC curve. RESULTS: 9 out of 105 effect risks factors were selected, in which career was the most important factor indicating that workers, teachers and retirees suffered much more risks than others. Nevertheless, the number of pregnancies, breast examination, reasons for menopause, age at menarche, intake of shrimp, crab, kipper, kelp and laver etc were also risk factors on breast cancer. However, physical exercise played different roles on different people. The Risk statistics of model was 0.174, and the area under the ROC curve was 0.872 which was significantly different from 0.5, suggesting that the classification tree model fit the actuality very well. CONCLUSION: The classification tree model could screen out the major affecting factors quickly and effectively and could also identify the cutting-points for continuous and ordinal variables, as well as revealing the complex interaction among the factors at many levels. This model might become a powerful tool to explore the complexities of the risks on diseases.
OBJECTIVE: To introduce the partitioning algorithm of classification tree model, and to explore the value of this data mining technique applied in data analysis of multifactorial diseases as malignant tumors. METHODS: Data was analyzed from a survey that conducted on 84 breast cancerpatients and 273 cancer-free controls selected randomly in Jiashan county. The classification tree model was constructed using Exhaustive CHAID method and evaluated by the Risk statistics and the area under the ROC curve. RESULTS: 9 out of 105 effect risks factors were selected, in which career was the most important factor indicating that workers, teachers and retirees suffered much more risks than others. Nevertheless, the number of pregnancies, breast examination, reasons for menopause, age at menarche, intake of shrimp, crab, kipper, kelp and laver etc were also risk factors on breast cancer. However, physical exercise played different roles on different people. The Risk statistics of model was 0.174, and the area under the ROC curve was 0.872 which was significantly different from 0.5, suggesting that the classification tree model fit the actuality very well. CONCLUSION: The classification tree model could screen out the major affecting factors quickly and effectively and could also identify the cutting-points for continuous and ordinal variables, as well as revealing the complex interaction among the factors at many levels. This model might become a powerful tool to explore the complexities of the risks on diseases.