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

doi:10.3969/j.issn.10062475.2018.04.022

Abstract

Abstract: 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 earlystage to endstage 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.

Key words: chronic kidney disease, progression forecast, logistic regression, ant path optimization, decision tree

CLC Number:

TP319

FENG Miao1, QI Xiaorong2, LI Zhi1. Progression Prediction Model of Chronic Kidney Disease Based on  Decision Tree Ant Path Optimization and Logistic Regression[J]. Computer and Modernization, 2018, 0(04): 117-.

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

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