Cardiovascular diseases, normally address
all kinds of diseases associated with the heart and is
being treated globally as the main cause of mortality.
Numerous risks are associated with heart diseases, a
need of the hour is to get worth, reasonable and
accurate early diagnosis method and treatment.
Machine Learning is being used in many areas to solve
such problems. The aim of this project is to predict the
heart disease in individuals. The use of many
classification algorithms in machine learning (ML) on
standard dataset has revealed that there is a need to
improve the accuracy as taking risk in heart related
diseases is not acceptable. The findings show that
highest accuracy is achieved through Decision Tress
Classifier (93%) and combining more than one method
is being used on same dataset in all possible
combinations and achieved an accuracy of 98.1%
keeping in mind, the time complexity of final algorithm
Supervised Learning, Classifier, Cardio Vascular Disease, Classificiation, Confusion Matrix.