重庆医学
重慶醫學
중경의학
CHONGQING MEDICAL JOURNAL
2013年
23期
2722-2724,2727
,共4页
灰色关联分析%偏最小二乘回归%住院费
灰色關聯分析%偏最小二乘迴歸%住院費
회색관련분석%편최소이승회귀%주원비
grey relational analysis%partial least squares regression%hospitalization expenses
目的结合灰色系统理论中的关联分析法与偏最小二乘回归模型,建立人均住院费用的预测模型。方法采用灰色关联分析筛选出人均住院费用的主要影响因子,对因子间进行共线性诊断,建立人均住院费用与主要影响因子间的偏最小二乘回归预测模型,通过实例证明该模型的预测精度。结果经灰色关联分析筛选出人均住院费用最主要影响因素为西药费、中药费,其次为诊疗费,其他费用、检查费、床费和手术费对人均住院费用影响也较大;偏最小二乘回归模型对住院费用拟合和预测准确率较高,平均相对误差较低,分别为-0.0002%、0.3493%。结论灰色关联分析与偏最小二乘回归适宜于住院费用影响因素与预测分析,可为样本量小、变量间存在严重共线性资料分析提供参考。
目的結閤灰色繫統理論中的關聯分析法與偏最小二乘迴歸模型,建立人均住院費用的預測模型。方法採用灰色關聯分析篩選齣人均住院費用的主要影響因子,對因子間進行共線性診斷,建立人均住院費用與主要影響因子間的偏最小二乘迴歸預測模型,通過實例證明該模型的預測精度。結果經灰色關聯分析篩選齣人均住院費用最主要影響因素為西藥費、中藥費,其次為診療費,其他費用、檢查費、床費和手術費對人均住院費用影響也較大;偏最小二乘迴歸模型對住院費用擬閤和預測準確率較高,平均相對誤差較低,分彆為-0.0002%、0.3493%。結論灰色關聯分析與偏最小二乘迴歸適宜于住院費用影響因素與預測分析,可為樣本量小、變量間存在嚴重共線性資料分析提供參攷。
목적결합회색계통이론중적관련분석법여편최소이승회귀모형,건립인균주원비용적예측모형。방법채용회색관련분석사선출인균주원비용적주요영향인자,대인자간진행공선성진단,건립인균주원비용여주요영향인자간적편최소이승회귀예측모형,통과실예증명해모형적예측정도。결과경회색관련분석사선출인균주원비용최주요영향인소위서약비、중약비,기차위진료비,기타비용、검사비、상비화수술비대인균주원비용영향야교대;편최소이승회귀모형대주원비용의합화예측준학솔교고,평균상대오차교저,분별위-0.0002%、0.3493%。결론회색관련분석여편최소이승회귀괄의우주원비용영향인소여예측분석,가위양본량소、변량간존재엄중공선성자료분석제공삼고。
Objective To combine grey relation analysis and partial least squares regression model to establish the forecasting model of per-patient hospitalization expenses .Methods Gray relational analysis was used to filter out the main factors affecting per-patient hospitalization expenses ,and then collinearity was examined between these factors .Partial least squares regression was used to establish prediction model of per-patient hospitalization expenses ,and the prediction accuracy was proved .Results After filtered by gray relational analysis ,the order of the importance of factors affecting per-patient hospitalization expenses was the west-ern medicine fee ,traditional Chinese medicine fees ,diagnosis and treat fees ,other fees ,inspection fees ,bed fees and operation fees . The established partial least squares regression model had a higher accuracy on fitting and prediction ,with low average relative er-ror ,respectively ,-0 .000 2% and 0 .349 3% .Conclusion The gray relational analysis and partial least squares regression are suit-able for the influencing factors and prediction analysis of hospitalization costs .It provides a reference for data with the small sample size and high collinearity between the variables .