Grey wolf optimized stacked ensemble machine learning based model for enhanced efficiency and reliability of predicting early heart disease

Abstract

Heart disease is one of the foremost reasons for death globally. Machine learning (ML) can be used to predict heart diseases early, which can help improve patient outcomes. This research proposes a novel machine learning method for predicting heart disease using a combination of Grey Wolf Optimization (GWO) and stacked ensemble techniques. GWO is a metaheuristic algorithm that can be used to optimize the parameters of machine-learning models. The stacked ensemble technique is a combination of multiple machine learning models to improve the overall accuracy of the prediction. The model proposed was evaluated using a dataset of heart patients. The results showed that the model achieved a 93% accuracy, which was significantly higher compared to traditional machine learning methods. The proposed method also had a higher precision of 91%, sensitivity of 95.3%, F1 score of 92.9%, and Matthew coefficient of 0.83, less in Log_Loss 2.87 than the traditional methods. The results of this research suggest that the proposed model is a promising new approach for predicting heart diseases. This method is more accurate and reliable than traditional methods and has the potential to improve patient outcomes.

KEYWORDS:

• Heart disease
• Grey Wolf Optimization
• stacked ensemble
• performance evaluation
• machine learning

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data considered for the study is taken from the UCI repository Cleveland dataset and can be accessed from (https://archive.ics.uci.edu/dataset/45/heart+disease)

Authors’ contributions

Geetha Narasimhan analyzed and interpreted the patient data regarding the heart disease and was a major contributor in writing the manuscript. Akila Victor has read and approved the final manuscript.