Izvorni znanstveni članak
https://doi.org/10.1080/00051144.2024.2317098
Grey wolf optimized stacked ensemble machine learning based model for enhanced efficiency and reliability of predicting early heart disease
Geetha Narasimhan
; School of Computer Science and Engineering, VIT, Vellore, Tamil Nadu, India
Akila Victor
; School of Computer Science and Engineering, VIT, Vellore, Tamil Nadu, India
*
* Dopisni autor.
Sažetak
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.
Ključne riječi
Heart disease; Grey Wolf Optimization; stacked ensemble; performance evaluation; machine learning
Hrčak ID:
326084
URI
Datum izdavanja:
26.2.2024.
Posjeta: 0 *