Cardiac arrest prediction in smokers using enhanced Artificial Bee Colony algorithm with stacked autoencoder model

Abstract

In the recent times, the cardiac arrest is a severe heart disease, which results in millions of annual casualties. In this article, the heart rate variability (HRV) parameters are used for predicting cardiac arrest in smokers based on the deep learning techniques. First, the input data is collected from MITU Skillogies dataset, which consists of 1562 smoker and non-smoker instances with 19 HRV input attributes/features. After data collection, the enhanced Artificial Bee Colony algorithm (EABC) is developed for feature selection. The EABC algorithm includes two new multi-objective functions for decreasing the number of attributes in the MITU Skillogies dataset. This mechanism superiorly reduces the burden of computational complexity and improves classification accuracy. Further, the selected attributes are given to the stacked autoencoder classifier for non-cardiac arrest and cardiac arrest classification in smokers for early diagnosis. The extensive experiment showed that the EABC with stacked autoencoder model obtained 96.26% of classification accuracy, which is better related to the traditional machine learning models.

Keywords:

• Artificial Bee Colony algorithm
• cardiac arrest prediction
• heart rate variability
• machine learning
• stacked autoencoder model

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.