Authors

Yanrong Li¹, Shibiao Xu¹, Li Guo¹, Qing Zhang², Hailong Jin², ¹University of Posts and Telecommunications, China, ²The Third Medical Center of PLA General Hospital, China

Abstract

Infection prediction after kidney transplantation is significant. Most existing models for predicting kidney transplant infection are statistical, unintelligent, and straightforward. The foremost task of this paper is to analyze kidney transplantation data, introduce existing traditional machine learning and deep learning methods from non-temporal and temporal scenarios, respectively, and comprehensively evaluate the predictive power of the methods for kidney transplantation infection. Specifically, in the non-temporal scenario, we use Naïve Bayes, K-Nearest Neighbor, Support Vector Machines, and Random Forest models. In addition, in the temporal scenario, we propose an MTAN model based on a sliding window algorithm to exploit the hidden information of adjacent time series fully. Experimental results show that the kidney transplantation prediction models built by Naïve Bayes and Support Vector Machines have better stability than those constructed by K-Nearest Neighbor and Random Forest. The MTAN model with sliding windows can better mine the hidden temporal information.

Keywords

Kidney Transplant Infection Prediction, Traditional Machine Learning, Deep Learning, Sliding Window.