Identification and Implementation Perspective on the Stratification Algorithms in the Prognostication of Heart Disease using Machine Learning Techniques
D. Vijay Lakshmi1, K. Yasudha2, Vanitha Kakollu3

1D Vijay Lakshmi*, PG Student, Department of CS,GIS,GITAM (Deemedto be University), Visakhapatnam, India.
2K Yasudha, Department of CS,GIS,GITAM (Deemed to be University), Visakhapatnam, India.
4Vanitha kakollu, Department of CS,GIS,GITAM (Deemed to be University), Visakhapatnam, India.
Manuscript received on December 19, 2019. | Revised Manuscript received on December 26, 2019. | Manuscript published on January 10, 2020. | PP: 1223-1225 | Volume-9 Issue-3, January 2020. | Retrieval Number: C8663019320/2020©BEIESP | DOI: 10.35940/ijitee.C8663.019320
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Abstract: Analysis of patient’s data is always a great idea to get accurate results on using classifiers. A combination of classifiers would give an accurate result than using a single classifier because one single classifier does not give accurate results but always appropriate ones. The aim is to predict the outcome feature of the data set. The “outcome” can contain only two values that is 0 and 1. 0 means patient doesn’t have heart disease and 1 means patient have heart diseases. So, there is a need to build a classification algorithm that can predict the Outcome feature of the test dataset with good accuracy. For this understanding the data is important, and then various classification algorithm can be tested. Then the best model can be selected which gives highest accuracy among all. The built model can then be given to the software developer for building the end user application using the selected machine learning model that will be able to predict the heart disease in a patient. 
Keywords: Random Forest Algorithm, SVM, Logistic Regression Algorithm, Machine Learning Classification.
Scope of the Article: Machine Learning