A novel Gini index decision tree data mining method with neural network classifiers for prediction of heart disease

2018 ◽  
Vol 22 (3) ◽  
pp. 225-242 ◽  
Author(s):  
K. Mathan ◽  
Priyan Malarvizhi Kumar ◽  
Parthasarathy Panchatcharam ◽  
Gunasekaran Manogaran ◽  
R. Varadharajan
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Heart Disease ◽  
Decision Tree ◽  
Gini Index ◽  
Mining Method ◽  
Data Mining Method ◽  
Tree Data ◽  
Neural Network Classifiers

Data mining method based on rough set and fuzzy neural network

2020 ◽  
Vol 38 (4) ◽  
pp. 3717-3725
Author(s):  
Jingyong Zhou ◽  
Yuan Guo ◽  
Yu Sun ◽  
Kai Wu
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Rough Set ◽  
Fuzzy Neural Network ◽  
Mining Method ◽  
Data Mining Method ◽  
Fuzzy Neural

Coaches Ranking: A Time-Irrelevant Data Mining Solution with Flexibility and Objectivity

2014 ◽  
Vol 989-994 ◽  
pp. 5010-5013
Author(s):  
Yong Yu Chen ◽  
Meng Jiao Zhang ◽  
Zhou Shen ◽  
Jian Xin Chen
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Regression Tree ◽  
Mining Method ◽  
Ranking System ◽  
Sports Teams ◽  
Ahp Model ◽  
Data Mining Method ◽  
Analysis System ◽  
Ranking Mechanism

The ranking of coaches is of great significance for sports teams to find talented coaches. Traditional ranking mechanism is relatively subjective which is usually accomplished by voting. In this paper, we establish a ranking mechanism based on both subjective and objective factors. We use data mining method to classify and analyze the data. The regression tree and the decision tree (CART) are used to narrow down the number of coaches to a reasonable scale. We put forward our AHP model to rank the “candidates” and propose a coach rating analysis system (CRAS) for evaluating the accuracy of system. We verify our ranking system more comprehensive in terms of the evaluation of the coaches through analyzing the result. The proposed mechanism is also significant to find potential coaches.

A Novel Relational Database Data Mining Method Based on Artificial Neural Network

2013 ◽  
Vol 380-384 ◽  
pp. 1860-1863
Author(s):  
Ping Zhang Gou ◽  
Yong Zhong Tang
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Artificial Neural Network ◽  
Relational Database ◽  
Training Model ◽  
The Novel ◽  
Mining Method ◽  
Data Mining Method ◽  
Artificial Neural Network Method ◽  
Artificial Neural

This study proposed a novel relational database data mining method based on the artificial neural network. It analyzed the disadvantages of the existed data mining methods and then introduced the novel algorithm. This algorithm discovered the implicit knowledge by training the data samples in the database. This study introduced the artificial neural network method training model and algorithm, and tested the method by an example.

scholarly journals Discovery of Diagnosis Pattern of Coronary Heart Disease with Qi Deficiency Syndrome by theT-Test-Based Adaboost Algorithm

2011 ◽  
Vol 2011 ◽  
pp. 1-7 ◽  
Author(s):  
Huihui Zhao ◽  
Jianxin Chen ◽  
Na Hou ◽  
Peng Zhang ◽  
Yong Wang ◽  
...  
Keyword(s):  
Data Mining ◽  
Coronary Heart Disease ◽  
Heart Disease ◽  
Cross Validation ◽  
Deficiency Syndrome ◽  
Biological Parameters ◽  
Mining Method ◽  
Adaboost Algorithm ◽  
Data Mining Method ◽  
Validation Technique

Coronary heart disease (CHD) is still the leading cause of death for adults worldwide. Traditional Chinese medicine (TCM) has a history of 1000 years fighting against the disease and provides a complementary and alternative treatment to it. Syndrome is the core of TCM diagnosis and it is traditionally diagnosed based on macroscopic symptoms as well as tongue and pulse recognitions of patients. Establishment of the diagnosis method in the microcosmic level is an urgent and major problem in TCM. The aim of this study was to establish characteristic diagnosis pattern for CHD with Qi deficiency syndrome (QDS). Thirty-four biological parameters were detected in 52 patients having unstable angina (UA) with or without QDS. Then, we presented a novel data mining method,t-test-based Adaboost algorithm, to establish highest prediction accuracy with the least number of biological parameters for UA with QDS. We gained a pattern composed of five biological parameters that distinguishes UA with QDS patients from non-QDS patients. The diagnosis accuracy of the patterns could reach 84.5% based on a 3-fold cross validation technique. Moreover, we included 85 UA cases collected from hospitals located in the north and south of China to further verify the association between the pattern and QDS. The classification accuracy is 83.5%, which keeps consistent with the accuracy obtained by the cross-validation technique. The association between a symptom and the five biological parameters was established by the data mining method and it reached an accuracy of ∼80%. These results showed that thet-test-based Adaboost algorithm might be a powerful technique for diagnosing syndrome in TCM in the context of CHD.

Sign in / Sign up

Export Citation Format

Share Document