计算机应用与软件
計算機應用與軟件
계산궤응용여연건
COMPUTER APPLICATIONS AND SOFTWARE
2014年
12期
221-224,282
,共5页
后天性脑损伤%认知功能康复%数据挖掘%决策树%多层感知器%广义回归神经网络
後天性腦損傷%認知功能康複%數據挖掘%決策樹%多層感知器%廣義迴歸神經網絡
후천성뇌손상%인지공능강복%수거알굴%결책수%다층감지기%엄의회귀신경망락
Acquired brain injury%Cognitive rehabilitation%Data mining%Decision tree%Multilayer perceptron%General regression
为了更好地预测后天性脑损伤ABI（ Acquired Brain Injury）患者认知功能康复的影响因素，提出基于决策树（ DT）、多层感知器（ MLP）和广义回归神经网络（ GRNN）的三种预测模型。借助于10折交叉验证测试算法，通过专一性、灵敏度和精度分析以及混淆矩阵分析对模型的性能进行测试，从而获得新的知识以评估和改善认知功能康复过程中的有效性。实验结果表明，基于DT的模型的模拟结果明显比其他模型更为优越，预测平均精度可高达90．38％。
為瞭更好地預測後天性腦損傷ABI（ Acquired Brain Injury）患者認知功能康複的影響因素，提齣基于決策樹（ DT）、多層感知器（ MLP）和廣義迴歸神經網絡（ GRNN）的三種預測模型。藉助于10摺交扠驗證測試算法，通過專一性、靈敏度和精度分析以及混淆矩陣分析對模型的性能進行測試，從而穫得新的知識以評估和改善認知功能康複過程中的有效性。實驗結果錶明，基于DT的模型的模擬結果明顯比其他模型更為優越，預測平均精度可高達90．38％。
위료경호지예측후천성뇌손상ABI（ Acquired Brain Injury）환자인지공능강복적영향인소，제출기우결책수（ DT）、다층감지기（ MLP）화엄의회귀신경망락（ GRNN）적삼충예측모형。차조우10절교차험증측시산법，통과전일성、령민도화정도분석이급혼효구진분석대모형적성능진행측시，종이획득신적지식이평고화개선인지공능강복과정중적유효성。실험결과표명，기우DT적모형적모의결과명현비기타모형경위우월，예측평균정도가고체90．38％。
To better predict the influencing factors of cognitive rehabilitation of acquired brain injury ( ABI) patients, we propose three prediction models which are based on decision tree (DT), multilayer perceptron (MLP) and general regression neural network (GRNN) respectively.By means of 10-fold cross validation test algorithm, we test the performance of the model by analysing its specificity, sensitivity and precision as well as the confusion matrix so as to obtain new knowledge to evaluate and improve the effectiveness of the cognitive rehabilitation process.Experimental results show that the simulation results based on DT model are clearly superior to other models, the averageprediction accuracy reaches up to 90.38%.
