  Heart Disease has become one of the most serious diseases that has a
significant impact on human life. It has emerged as one of the leading causes
of mortality among the people across the globe during the last decade. In order
to prevent patients from further damage, an accurate diagnosis of heart disease
on time is an essential factor. Recently we have seen the usage of non-invasive
medical procedures, such as artificial intelligence-based techniques in the
field of medical. Specially machine learning employs several algorithms and
techniques that are widely used and are highly useful in accurately diagnosing
the heart disease with less amount of time. However, the prediction of heart
disease is not an easy task. The increasing size of medical datasets has made
it a complicated task for practitioners to understand the complex feature
relations and make disease predictions. Accordingly, the aim of this research
is to identify the most important risk-factors from a highly dimensional
dataset which helps in the accurate classification of heart disease with less
complications. For a broader analysis, we have used two heart disease datasets
with various medical features. The classification results of the benchmarked
models proved that there is a high impact of relevant features on the
classification accuracy. Even with a reduced number of features, the
performance of the classification models improved significantly with a reduced
training time as compared with models trained on full feature set.
