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为比较灰色动态模型对 １０ 种主要恶性肿瘤死亡率的拟和及预测效果，利用山东省某县１９７４～１９９２ 年 １０ 种主要恶性肿瘤死亡率资料，分别拟和 Ｇ Ｍ （１，１）模型。结果表明，只有肝癌、肺癌死亡率的 Ｇ Ｍ （１，１）模型拟合效率较好，可用于预测；其他 ８ 种恶性肿瘤死亡率资料的 Ｇ Ｍ （１，１）模型拟合效果不理想，不能用于预测。用 Ｇ Ｍ （１，１）模型对该地未来 ８ 年的肝癌、肺癌死亡率进行预测，发现在未来几年内如果不实施强有力的干预措施，两种肿瘤均将呈持续上升趋势。比较原始数据的变化趋势， Ｇ Ｍ （１，１）模型对于稳定上升的资料拟和效果较好，对波动性较大，如一些流行因素变化较大，或采用新的防疫措施的疾病，其适用性有待进一步探讨。
In order to compare the prediction and prediction effect of the gray dynamic model on the mortality of the 10 major malignant tumors, the data of 10 major malignant tumor mortality rates from 1974 to 1992 in a county in Shandong Province were used to model the G M (1,1) model, respectively. The results show that only the G M (1,1) model for liver cancer and lung cancer mortality has a good fitting efficiency and can be used for prediction; the fitting effect of G M (1,1) model for other 8 malignant tumor mortality data is not ideal Cannot be used for forecasting. Using the G M (1,1) model to predict the mortality of liver cancer and lung cancer in the next 8 years, it is found that if no strong interventions are implemented in the next few years, both tumors will continue to rise. Compared with the change trend of the original data, the G M (1,1) model has a better effect on the steady rise of the data and has greater volatility, such as some popular factors, or new disease prevention measures. Applicability remains to be further explored.