Travel-Hunt-Based Deep CNN Classifier: A Nature-Inspired Optimization Model for Heart Disease Prediction

Bhavekar, Girish S.; Goswami, Agam Das

doi:10.1080/03772063.2023.2215736

Cited by 1 publication

(1 citation statement)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Dhaka and Nagpal ( 10 ) presented a model using deep BiLSTM combined with Whale-on-Marine optimization, achieving 97.53% accuracy across multiple datasets. Bhavekar and Goswami ( 11 ) introduced the travel-hunt-DCNN classifier, marking 96.665% accuracy on a specific dataset. Jayasudha et al ( 12 ) further developed a hybrid optimization deep learning-based ensemble classification, achieving a commendable 95.36% sensitivity.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective artificial bee colony optimized hybrid deep belief network and XGBoost algorithm for heart disease prediction

Kalita,

Ganesh,

Jayalakshmi

et al. 2023

Front. Digit. Health

View full text Add to dashboard Cite

The global rise in heart disease necessitates precise prediction tools to assess individual risk levels. This paper introduces a novel Multi-Objective Artificial Bee Colony Optimized Hybrid Deep Belief Network and XGBoost (HDBN-XG) algorithm, enhancing coronary heart disease prediction accuracy. Key physiological data, including Electrocardiogram (ECG) readings and blood volume measurements, are analyzed. The HDBN-XG algorithm assesses data quality, normalizes using z-score values, extracts features via the Computational Rough Set method, and constructs feature subsets using the Multi-Objective Artificial Bee Colony approach. Our findings indicate that the HDBN-XG algorithm achieves an accuracy of 99%, precision of 95%, specificity of 98%, sensitivity of 97%, and F1-measure of 96%, outperforming existing classifiers. This paper contributes to predictive analytics by offering a data-driven approach to healthcare, providing insights to mitigate the global impact of coronary heart disease.

show abstract

Section: Introductionmentioning

confidence: 99%