Early Detection of Clinical Parameters in Heart Disease by Improved Decision Tree Algorithm

2010 ◽  
Author(s):  
Ali Mirza Mahmood ◽  
Mrithyumjaya Rao Kuppa
Keyword(s):  
Heart Disease ◽  
Early Detection ◽  
Decision Tree ◽  
Clinical Parameters ◽  
Decision Tree Algorithm ◽  
Tree Algorithm

scholarly journals Heart Disease Prediction Using Machine Learning Algorithm

2019 ◽  
Vol 8 (10) ◽  
pp. 2603-2606
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Decision Tree ◽  
Learning Algorithm ◽  
Heart Diseases ◽  
Support Vector ◽  
Machine Learning Algorithm ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Tree Classifier

Heart disease is a common problem which can be very severe in old ages and also in people not having a healthy lifestyle. With regular check-up and diagnosis in addition to maintaining a decent eating habit can prevent it to some extent. In this paper we have tried to implement the most sought after and important machine learning algorithm to predict the heart disease in a patient. The decision tree classifier is implemented based on the symptoms which are specifically the attributes required for the purpose of prediction. Using the decision tree algorithm, we will be able to identify those attributes which are the best one that will lead us to a better prediction of the datasets. The decision tree algorithm works in a way where it tries to solve the problem by the help of tree representation. Here each internal node of the tree represents an attribute, and each leaf node corresponds to a class label. The support vector machine algorithm helps us to classify the datasets on the basis of kernel and it also groups the dataset using hyperplane. The main objective of this project is to try and reduce the number of occurrences of the heart diseases in patients

scholarly journals hs-CRP is strongly associated with coronary heart disease (CHD): A data mining approach using decision tree algorithm

2017 ◽  
Vol 141 ◽  
pp. 105-109 ◽  
Author(s):  
Maryam Tayefi ◽  
Mohammad Tajfard ◽  
Sara Saffar ◽  
Parichehr Hanachi ◽  
Ali Reza Amirabadizadeh ◽  
...  
Keyword(s):  
Data Mining ◽  
Coronary Heart Disease ◽  
Heart Disease ◽  
Decision Tree ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Data Mining Approach ◽  
Hs Crp

scholarly journals Improving the Efficiency by Novel Feature Extraction Technique Using Decision Tree Algorithm Comparing with SVM Classifier Algorithm for Predicting Heart Disease

2021 ◽  
Vol 36 (1) ◽  
pp. 713-720
Author(s):  
S.K.L. Sameer ◽  
P. Sriramya
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Heart Disease ◽  
Decision Tree ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Svm Classifier ◽  
Decision Tree Algorithm ◽  
Tree Algorithm

Aim: The objective of the research work is to use the two machine learning algorithms Decision Tree(DT) and Support vector machine(SVM) for detection of heart disease on earlier stages and give more accurate prediction. Materials and methods: Prediction of heart disease is performed using two machine learning classifier algorithms namely, Decision Tree and Support Vector Machine methods. Decision tree is the predictive modeling approach used in machine learning, it is a type of supervised machine learning. Support-vector machines are directed learning models with related learning calculations that break down information for order and relapse investigation. The significance value for calculating Accuracy was found to be 0.005. Result and discussion: During the process of testing 10 iterations have been taken for each of the classification algorithms respectively. The experimental results shows that the decision tree algorithm with mean accuracy of 80.257% is compared with the SVM classifier algorithm of mean accuracy 75.337% Conclusion: Based on the results achieved the Decision Tree classification algorithm better prediction of heart disease than the SVM classifier algorithm.

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