A Gradient Boosted Decision Tree with Binary Spotted Hyena Optimizer for cardiovascular disease detection and classification

Kiran, Siripuri; Reddy, G. Raghotham; S.P., Girija; S., Venkatramulu; Dorthi, Kumar; V., Chandra Shekhar Rao

doi:10.1016/j.health.2023.100173

Cited by 10 publications

(2 citation statements)

References 23 publications

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“…XGBoost is a machine learning method based on a gradient framework ensemble learning algorithm that predicts the residual of the current model by creating a new model and then adding all the models together in a serial manner as the nal prediction. The XGBoost classi cation model has been widely used in the medical eld in recent years and has been used for the classi cation of cardiovascular diseases [14] , cancer [15] and other types of cancer. In this study, the obtained remote sensing image data were input into the DeepLabv3 + model to predict regional land features, after which an XGBoost classi cation model was established with point land feature data as the independent variable and mosquito data as the dependent variable.…”

Section: Introductionmentioning

confidence: 99%

A Mosquito Distribution Prediction Method Based on High-Resolution Remote Sensing Maps and the Combination of Deep Learning and Machine Learning

Liu,

Zhu,

Sun

et al. 2024

Preprint

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Objective To establish a mosquito distribution prediction model using high-resolution remote sensing maps and machine learning deep learning to determine the risk distribution of mosquito-borne infectious disease vectors and provide a decision-making basis for precise prevention and control. Method From March to November 2017–2021, a mosquito survey was conducted at 20 mosquito monitoring points in Wuxi City. High-resolution remote sensing image semantic segmentation was used to identify the ground features of a 250 meter radius buffer zone at the mosquito monitoring points. The typical correlation between the number of mosquitoes and ground features was analyzed, and an XGBoost model was established. Results A total of 5 types of mosquitoes were observed through the investigation, namely, Culex pipiens pallens, Culex trituberculatus, Anopheles sinensis, Aedes albopictus, and Aedes disturbance, representing 56.5%, 31.7%, 5.0%, 4.0%, and 1.7% of the observed mosquitos, respectively. The accuracy of the land feature data obtained by machine recognition and manual interpretation reaches 70%. The correlation coefficient between the ground feature data and mosquito distribution is 0.892. The accuracy, recall, precision, and F1-score of the dominant species prediction models are 0.916, 0.875, 0.857, and 0.865, respectively, while those for the common species are 0.758, 0.669, 0.733, and 0.699, respectively. Conclusion The prediction model established by machine learning deep learning can effectively predict the distribution of mosquitoes and provide a decision-making basis for precise prevention and control.

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Section: Introductionmentioning

confidence: 99%

A Mosquito Distribution Prediction Method Based on High-Resolution Remote Sensing Maps and the Combination of Deep Learning and Machine Learning

Liu,

Zhu,

Sun

et al. 2024

Preprint

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show abstract

“…Rajkumar et al ( 17 ) ventured into IoT-based heart disease prediction using deep learning, marking 98.01% accuracy. Kiran et al ( 18 ) specifically explored the effectiveness of machine learning classifiers for prediction CVD, proposing the GBDT-BSHO approach and achieving 97.89% accuracy.…”

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

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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.

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A hybrid machine learning and natural language processing model for early detection of acute coronary syndrome

Emakhu,

Etu,

Monplaisir

et al. 2023

Healthcare Analytics

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A Gradient Boosted Decision Tree with Binary Spotted Hyena Optimizer for cardiovascular disease detection and classification

Cited by 10 publications

References 23 publications

A Mosquito Distribution Prediction Method Based on High-Resolution Remote Sensing Maps and the Combination of Deep Learning and Machine Learning

A Mosquito Distribution Prediction Method Based on High-Resolution Remote Sensing Maps and the Combination of Deep Learning and Machine Learning

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

A hybrid machine learning and natural language processing model for early detection of acute coronary syndrome

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