This research is designed in alignment with recent reports published by
World Health Organization (WHO) on Cardio vascular diseases (CVD) the
most common heart problem and the possible detection methods. Recent
estimates show that 27.1 million individuals are suffering from CVD in 2018
out of which 69% of them suffer death as an average in the world every year
due to cardiac trauma. Third world countries with low and moderate incomes
are affected adversely at 82% of death caused by CVD occurs in these
countries out of the world total numbers irrespective of gender. It is estimated
to be about 23,600,000 in 2030 individuals would suffer from heart diseases.
Southeast Asia and eastern Mediterranean region is the most prominent areas
of concern because of changes of life styles, food habits, and occupational
culture. Heart diseases mainly affect individuals with 65 years old and older
but still expanding in developing countries due to lack of health care in these
countries.
The recent reports of WHO also indicates, the need for accurate
methods for prediction at early stage by efficient periodical examination of
heart to diagnose heart diseases are very crucial for health care planning of
such countries. The number and size of medical databases are rapidly
increasing, and the advanced models of data mining techniques could help
physicians to make efficient and applicable decisions. The challenges of heart
disease data include the feature selection, the number of the samples;
imbalance of the samples, lack of magnitude for some features, etc. This
study mainly focuses on the feature selection improvement and decreasing the
numbers of the features. In this study, meta-heuristic approach is suggested in
order to select prominent features of the heart disease. Evaluation result
shows that by using the proposed algorithm, the accuracy of feature selection
technique has been improved.
