heuristic approaches for heart disease classification-ok

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.