Application Of Random Forest Algorithms In Rehabilitation Planning Of AIDS Patients In Urology

AIDS is a harmful infectious disease that needs global attention.It is easy to cause various infections and malignant tumors after triggering.In Guangdong Province,the number of patients with AIDS in urology department has also increased significantly in recent years.Therefore,how to provide more professional patient care for the increasing number of AIDS urological patients arises at the historic moment.Patient care plan includes disease diagnosis,operation control,emotion management,virus monitoring and recovery estimation.In this paper,3443 pieces of first-hand clinical data were obtained from urology department of a large-scale third class hospital,including 6 links of data: situation analysis,preoperative preparation,operation mode,intraoperative management,postoperative treatment and rehabilitation plan.This paper focuses on the study of patients' disease,operation and wound healing data,and finally forms reasonable suggestions for patients' care plan.Starting from machine learning,this paper studies and analyzes clinical data with the help of data mining software Weka system platform.Because Weka system has a weak ability to process Chinese characters,the necessary data preprocessing is carried out at first.The features of Cross-repetition in data are merged,the features of missed or missed filling are supplemented,and the Chinese information content in the original data is converted by a string of letters combined with numbers.Because of the large dimension of data and the complexity of manual processing,a small program is written in Python language to assist data processing into a large matrix,so that it can be easily imported into Weka system for data mining.In view of the excessive dimension,there is more noise in large matrix.In experiment 1,the random attribute algorithm is used to reduce the dimension of data and extract the principal component attributes,which is conducive to the subsequent discovery of implicit knowledge.Experiments 2 classified and predicted wound healing after urological surgery.Random forest was selected as the basic classification model by comparing the advantages and disadvantages of IBk,neural network and random forest.Then,the performance of the model was improved by fine-tuning parameters and the classification test was well completed.Experiments 3 used FPGrowth algorithm to analyze the correlation of common diseases of AIDS patients.Through the interpretation of association rules,we found a statistical association between AIDS patients and kidney stone disease.Through the deep mining of the first-line clinical data,we found an effective model for predicting wound healing,and found an implicit rule to provide more decision support for individualized patient care in the whole process of urological AIDS patients.