Knowledge discovery in heart disease dataset

A cardiovascular disease is one of the most significant causes of mortality in today's world. Cardiovascular diseases are the number one cause of death globally with 17.9 million death cases each year. CVDs are concertedly contributed by hypertension, diabetes, overweight and unhealthy lifestyles. Exploratory Data Analysis is a pre-processing step to understand the data. There are numerous methods and steps in performing EDA, however, most of them are specific, focusing on visualization and distribution. This research work finds that the optimal solution of this dataset. BayesNet, VotedPEreception, LWL, Weighted InstanceHandleWrapper and Decisionable classifiers have respectively 81.46% of accuracy level, 81.44% of accuracy level, 82.81% of accuracy level, 82.43% of accuracy level at the implementation of 40:60 Cross validation. Which is having the high accuracy level in all classifiers what we implemented except Decision Stump Algorithm. BayesNetPrduces above 81% of accuracy level at the 50:50 Cross validation; Bayes Net classifier has 81.46%,VotedPerceptorn Classifier has 81.44% of accuracy level, LWL classifier has 82.81% of accuracy level, and WeightedInstanceHandleWrapper classifier has 82.43% of accuracy level; DecisionStump classifier has 81.77% of accuracy level at the 10:90 Cross validation.