The Research on the Boosting Decision Tree Algorithm for Intelligent Medical System

2014 ◽  
Vol 539 ◽  
pp. 365-368
Author(s):  
Dong Lan Zou

Intelligent Medical Systems currently has been used in most advanced hospitals worldwide, and most of theses systems have the function module of guiding-patient-diagnosis, which makes the patient use this feature to get a preliminary treatment opinion based on their patients condition. In this paper, the way of enhancing the effect of guiding-patient-diagnosis with boosting decision tree algorithm is researched, and the paper describes the specific applying process of the algorithm. Finally, boosting decision tree algorithm prove to improve the efficiency of intelligent guiding function, making intelligent guiding diagnosis function accurate and reliable, which save the health care resources and the time of doctors and patients.

2013 ◽  
Vol 347-350 ◽  
pp. 3397-3402 ◽  
Author(s):  
Zhi Yuan Liu ◽  
Jian Xi Peng ◽  
Yuan Kai Yang

In the medical system, medical record is summarized, analyzed and predicted a patient seizure type and incidence. With the development of information technology, a large amount of data is to be processed. Traditional analysis algorithm could not be effectively processed to obtain the best predictive results as the data increasing. Decision tree algorithm based on cloud platform is used to record, analyze and predict patients medical data in this paper. A large number of experimental results show that distributed decision tree algorithm proposed in this paper is efficient and could complete prediction work in medical system. The algorithm has good expansibility, its very suitable for large-scale and multitude medical data process.


2021 ◽  
Vol 29 (4) ◽  
Author(s):  
Ashok Kumar ◽  
Arun Lal Srivastav ◽  
Ishwar Dutt ◽  
Karan Bajaj

The high rate of urbanisation has increased the need for state-of-art health models that can meet the growing needs of society during any pandemic. Information-theoretic algorithms based on decision tree can mine the data to establish standards for the final decision by classifying the related data. Classification is an effective tool to analyse the existing health system in India’s states and union territories. For this purpose, the data is categorised and then treated with the enhanced Shannon Entropy-based C4.5 decision tree algorithm to set some rules. These rules are capable of finding the major gaps in the health care systems after the analysis. Supposedly, these gaps are taken care of properly in the affected regions. In that case, the health care models will accomplish the endeavouring Sustainable Development Goals.


2021 ◽  
Vol 1869 (1) ◽  
pp. 012082
Author(s):  
B A C Permana ◽  
R Ahmad ◽  
H Bahtiar ◽  
A Sudianto ◽  
I Gunawan

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