Analysis and Graphical Representation of Data Mining Techniques for Prediction of Heart Disease Using the Weka Tool
1K.AshaRani , Dr A.V.R Mayuri , Dr C. Sreedhar
In current decades, heart disease has been recognized as like the leading cause of death throughout the world. However, it is considered so the most preventable or controllable disease at the same time. According in conformity with World Health Organization (WHO), the express and timely analysis of heart disease plays a remarkable role within preventing its progress and reducing related treatment costs. Data mining methods and machine learning algorithms play a very important function within this area. The researchers accelerating their research works to boost a software with the help machine learning algorithm which perform help doctors to take a decision regarding both prediction and diagnosing of heart disease. The main objective over this research paper is predicting the heart disease regarding a patient the use of machine learning algorithms. Comparative study concerning Naïve Base Classifier, K-nearest neighbor, Support vector machines and Random Forest the precision and recall about machine learning algorithms is performed through a graphical representation concerning the results.
Data mining, Heart disease, WEKA, Naïve Base Classifier, K-nearest neighbor, Support vector machines and Random Forest