Abstract
Using echocardiography flexible Transthoracic Echocardiography reported data set detecting heart disease by using mining techniques designed prediction model the data set can develop the reliability of analysis of cardiac diseases by echocardiography, using eight iterative and interactive steps consisting Knowledge Discovery in Database (KDD) methodology including from 209 patients with echocardiography to extracting the data important mode of action Transthoracic Echocardiography inspection report. This study used data from Faisalabad Institute of Cardiology study from 2012 to 2015. All models exposed the results of J48 decision tree, naive bayes classifier and neural network that has extraordinary classification precision and predictive of heart disease cases are generally comparable. However, J48 model predictive classification accuracy shows of 80% based on the true positive rate ratio and performance slightly better. This study shows to predict heart disease cases and People can be used the results of our study to make more consistent diagnosis of cardiac disease and to help them as a support tool for cardiac disease specialists.
Highlights
Heart disease causes higher mortality rate in our Pakistan
In addition the results show the impact of attributes select the classification accuracy, the size of the decision tree and the complexity of the model
Naïve Bayes classifier to achieve the highest accuracy in the all property (80%) while a Naïve Bayes classifier to achieve a selected attribute it is a 78% of the sub-class accuracy followed On the other hand simple two implement a decision tree classifier score and the entire group selected attribute properties lowest sub class accuracy which were 75% and 77%
Summary
Heart disease causes higher mortality rate in our Pakistan. In our country the male and female having the age 65-year-old they are facing the heart disease. Data mining technology technique is used to decrease cardiac disease in entirely over the world. Researcher can identify heart diseases by skillfully doctor through extreme risk factors. To choose the best predictive method researcher use various data mining techniques to predict cardiac diseases at this end. The Manimekalai [1] says that different risky aspects in the manner that smoking, high blood pressure, diabetes, obesity did not increase heart diseases
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