Application of Drug Allocation Treatment Based on Decision Tree Algorithm

Abstract

 Drug testing has always been an effective way to test drug effects and reduce drug costs. It is also a necessary measure for testing in the clinical stage. The results of drug testing will provide an important basis for the listing of new drugs, which is of great significance for promoting the development of medical science and improving the quality of life of patients. This article uses the decision tree algorithm to achieve effective allocation of drugs, improve the accuracy of treatment, determine and quantify the patient's key parameter information, including gender, blood pressure, and cholesterol. The model introduces two kinds of algorithm forms of decision tree through information entropy and Gini coefficient, and divides the data set into five kinds of decision trees through the algorithm, and the accuracy values all reach above 85%, and wait until the approximate accuracy effect under the condition of specifying random numbers, which provides scientific analysis for analyzing patients' choice of drugs.