Neural Networks predicted heart disease with the highest accuracy (100%), outperforming Decision Trees (99.62%) and Naive Bayes (90.74%) when using 15 input attributes.
Does adding obesity and smoking to 13 standard attributes improve the accuracy of heart disease prediction using data mining techniques?
Adding obesity and smoking to standard clinical attributes improves the accuracy of machine learning models, particularly Neural Networks, in predicting heart disease.
Absolute Event Rate: 100% vs 99.62%
The Healthcare industry is generally "information rich", but unfortunately not all the data are mined which is required for discovering hidden patterns & effective decision making. Advanced data mining techniques are used to discover knowledge in database and for medical research, particularly in Heart disease prediction. This paper has analysed prediction systems for Heart disease using more number of input attributes. The system uses medical terms such as sex, blood pressure, cholesterol like 13 attributes to predict the likelihood of patient getting a Heart disease. Until now, 13 attributes are used for prediction. This research paper added two more attributes i.e. obesity and smoking. The data mining classification techniques, namely Decision Trees, Naive Bayes, and Neural Networks are analyzed on Heart disease database. The performance of these techniques is compared, based on accuracy. As per our results accuracy of Neural Networks, Decision Trees, and Naive Bayes are 100%, 99.62%, and 90.74% respectively. Our analysis shows that out of these three classification models Neural Networks predicts Heart disease with highest accuracy.
S.Dangare et al. (2012) studied Heart disease. Data mining classification techniques (Neural Networks, Decision Trees, Naive Bayes) vs. Comparison among techniques was evaluated on Prediction accuracy. Neural Networks predicted heart disease with the highest accuracy (100%), outperforming Decision Trees (99.62%) and Naive Bayes (90.74%) when using 15 input attributes.