Hybrid Optimization based Feature Selection with DenseNet Model for Heart Disease Prediction

Krishnan, V. Gokula; Saradhi, M. V. Vijaya; Kumar, S. Sai; Dhanalakshmi, G.; Parajuli, PK; Vijayaraja, V.

doi:10.37391/ijeer.110203

Cited by 3 publications

(1 citation statement)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Asif et al ( 14 ) utilized the extra tree classifier in their machine learning model, achieving 98.15% accuracy. Krishnan et al ( 15 ) proposed a model using transfer learning and hybrid optimization, emphasizing both reduced training time and improved accuracy. Yaqoob et al ( 16 ) presented a unique hybrid framework addressing both privacy concerns and communication costs, improving prediction accuracy by 1.5%.…”

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%

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

show abstract

Enhancing cardiac diagnostics through semantic-driven image synthesis: a hybrid GAN approach

Gurusubramani,

Latha

2024

Neural Comput & Applic

View full text Add to dashboard Cite

An Adaptive Grid Search Based Efficient Ensemble Model for Covid-19 Classification in Chest X-Ray Scans

Naresh,

Visalakshi

2023

IJEER

View full text Add to dashboard Cite

Covid has resulted in millions of deaths worldwide, making it crucial to develop fast and safe diagnostic methods to control its spread. Chest X-Ray imaging can diagnose pulmonary diseases, including Covid. Most research studies have developed single convolution neural network models ignoring the advantage of combining different models. An ensemble model has higher predictive accuracy and reduces the generalization error of prediction. We employed an ensemble of Multi Deep Neural Networks models for Covid.19 classification in chest X-Ray scans using Multiclass classification (Covid, Pneumonia, and Normal). We improved the accuracy by identifying the best parameters using the sklean Grid search technique and implementing it with the Optimized Weight Average Ensemble Model, which allows multiple models to predict. Our ensemble model has achieved 95.26% accuracy in classifying the X-Ray images; it demonstrates potential in ensemble models for diagnosis using Radiography images.

show abstract

Hybrid Optimization based Feature Selection with DenseNet Model for Heart Disease Prediction

Cited by 3 publications

References 28 publications

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

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

Enhancing cardiac diagnostics through semantic-driven image synthesis: a hybrid GAN approach

An Adaptive Grid Search Based Efficient Ensemble Model for Covid-19 Classification in Chest X-Ray Scans

Contact Info

Product

Resources

About