基于蚁群路径优化决策树及逻辑回归的慢性肾病进展概率预测模型

Progression Prediction Model of Chronic Kidney Disease Based on Decision Tree Ant Path Optimization and Logistic Regression

慢性肾病(Chronic Kidney Disease,CKD)是一种进展性疾病,早期若不及时加以治疗会导致病情发展,甚至肾衰竭。为了研究CKD患者从早期发展到终末期的概率,本文提出一种CKD进展概率预测模型:结合蚁群路径优化决策树算法(Decision Tree Ant Path Optimization,DTAPO)和逻辑回归算法(Logistic Regression,LR),将CKD患者数据分为P(进展)和NP(非进展)2类,得到分类精确率和召回率,从而计算CKD患者由3期进展到4期或5期的概率。实验结果表明,当特征数目为13时,结合逻辑回归的蚁群路径优化决策树算法的预测效果最好,其分类精确率为98.84%,由该精确率预测得到的进展患者确实由3期进展到4期或5期的概率为0.9827。

Chronic kidney disease(CKD) is a progressive disease,it will lead to the development of the disease and even renal failure if not treated in a timely manner. To study the progression probability from early-stage to end-stage of CKD patients,a prediction model of CKD progression probability is proposed. Combining decision tree ant path optimization(DTAPO) and logistic regression(LR) algorithm,this paper divides CKD patients' data into two categories: progress(P) and non progress(NP),the classification accuracy rate and recall rate are obtained so as to calculate the probability from the stage 3 to the stage 4 or 5. It is demonstrated from the experimental results that when the number of features is 13,the prediction algorithm combining decision tree ant path optimization algorithm with logistic regression achieves the best performance,and the accuracy rate of classification is 98. 84%. The probability of progression from the stage 3 to the stage 4 or 5 is 0. 9827.