Combination Data Mining Methods with New Medical Data to Predicting Outcome of Coronary Heart Disease

2007 ◽  
Author(s):  
Yanwei Xing ◽  
Jie Wang ◽  
Zhihong Zhao ◽  
andYonghong Gao
Keyword(s):  
Data Mining ◽  
Coronary Heart Disease ◽  
Heart Disease ◽  
Medical Data ◽  
Predicting Outcome ◽  
Mining Methods

scholarly journals Applications of Data Mining Methods in the Integrative Medical Studies of Coronary Heart Disease: Progress and Prospect

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Yan Feng ◽  
Yixin Wang ◽  
Fang Guo ◽  
Hao Xu
Keyword(s):  
Data Mining ◽  
Coronary Heart Disease ◽  
Heart Disease ◽  
Integrative Medicine ◽  
External Validity ◽  
Controlled Trials ◽  
Medical Studies ◽  
Data Types ◽  
Randomized Controlled ◽  
Mining Methods

A large amount of studies show that real-world study has strong external validity than the traditional randomized controlled trials and can evaluate the effect of interventions in a real clinical setting, which open up a new path for researches of integrative medicine in coronary heart disease. However, clinical data of integrative medicine in coronary heart disease are large in amount and complex in data types, making exploring the appropriate methodology a hot topic. Data mining techniques are to analyze and dig out useful information and knowledge from the mass data to guide people’s practices. The present review provides insights for the main features of data mining and their applications of integrative medical studies in coronary heart disease, aiming to analyze the progress and prospect in this field.

scholarly journals The Comparison of Data Mining Methods Using C4.5 Algorithm and Naive Bayes in Predicting Heart Disease

Tech-E ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 44
Author(s):  
Rino Rino
Keyword(s):  
Data Mining ◽  
Heart Disease ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Data Set ◽  
A Value ◽  
C4.5 Algorithm ◽  
Calculation Results ◽  
Mining Methods ◽  
Bayes Algorithm

Heart disease is a condition of the presence of fatty deposits in the coronary arteries in the heart which changes the role and shape of the arteries so that blood flow to the heart is obstructed. Data mining methods can predict this disease, some of the methods are C4.5 Algorithm and Naive Bayes which are often used in research.The data set in this research was obtained from the uci machine learning repository site, where the dataset has 3546 records and 13 attributes.The accuracy value of the Naïve Bayes algorithm has a high value of 81.40% compared to the C4.5 algorithm which only has an accuracy value of 79.07%. Based on the calculation results, it can be concluded that the Naïve Bayes Algorithm is a very good clarification because it has a value between 0.709 - 1.00.From conclusion above, the Naïve Bayes algorithm has a higher accuracy value than the C4.5 algorithm so the researchers decided to use the Naïve Bayes algorithm in predicting heart disease.

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