An intelligent heart disease prediction system using hybrid deep dense Aquila network

Barfungpa, Sonam Palden; Sarma, Hiren Kumar Deva; Samantaray, Leena

doi:10.1016/j.bspc.2023.104742

Cited by 15 publications

(7 citation statements)

References 37 publications

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“…The model novelty helped the authors to achieve an accuracy of 98 ± 0.9%. However, the works reported by Barfungpa et al (2023) and Alam and Muqeem (2023) could not converge during optimization.…”

Section: Related Workmentioning

confidence: 97%

“…In discussing the use of ML models, Miriyala et al (2021) used a single LGBM to diagnose CAD in 1,190 instances, reporting 93.3% accuracy; the model has an overfit issue, which can be solved through optimization. Barfungpa et al (2023) optimized features using an enhanced sparrow search algorithm (E-SSA). Their focus was to minimize the dimensionality of data using deepdense residual attention Aquila convolutional network (Deep-DenseAquilaNet).…”

Section: Related Workmentioning

confidence: 99%

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PSO-XnB: a proposed model for predicting hospital stay of CAD patients

Miriyala,

Sinha

2024

Front. Artif. Intell.

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Coronary artery disease poses a significant challenge in decision-making when predicting the length of stay for a hospitalized patient. This study presents a predictive model—a Particle Swarm Optimized-Enhanced NeuroBoost—that combines the deep autoencoder with an eXtreme gradient boosting model optimized using particle swarm optimization. The model uses a fuzzy set of rules to categorize the length of stay into four distinct classes, followed by data preparation and preprocessing. In this study, the dimensionality of the data is reduced using deep neural autoencoders. The reconstructed data obtained from autoencoders is given as input to an eXtreme gradient boosting model. Finally, the model is tuned with particle swarm optimization to obtain optimal hyperparameters. With the proposed technique, the model achieved superior performance with an overall accuracy of 98.8% compared to traditional ensemble models and past research works. The model also scored highest in other metrics such as precision, recall, and particularly F1 scores for all categories of hospital stay. These scores validate the suitability of our proposed model in medical healthcare applications.

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Section: Related Workmentioning

confidence: 97%

Section: Related Workmentioning

confidence: 99%

PSO-XnB: a proposed model for predicting hospital stay of CAD patients

Miriyala,

Sinha

2024

Front. Artif. Intell.

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“…By assessing their predictive accuracy in determining the probability of heart disease development, we seek to delineate the most adept predictive modeling approaches. Through this thorough analysis and comparative study, our goal is to unveil the optimal strategies For precise and reliable heart disease prediction: Exploring machine learning's potential [5]. In our exploration of predictive modeling, we aim to examine how patient attributes and medical history influence the prediction of heart diseases.…”

Section: A the Objectives Of Our Study Are Twofoldmentioning

confidence: 99%

Prediction Of Cardiovascular Disorders Using Machine Learning

Ramya Sri Bhuvana,

Rohith,

Swathi

et al. 2024

eatp

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Heart diseases are one of the world's most pressing health challenges numerous deaths each year. They encompass various cardiovascular conditions like Coronary Artery disease, heart failure, and arrhythmias. Detecting these diseases early is crucial for effectively managing and preventing complications. Predictive modeling emerges as a pivotal tool in this endeavor, utilizing advanced data analysis methodologies to anticipate the probability of encountering heart-related conditions. Timely detection facilitates prompt intervention, empowering healthcare practitioners to enact preventive strategies and customize treatment regimens to suit each patient's unique requirements. Predictive modeling serves as a valuable tool in pinpointing individuals at elevated risk of heart diseases, leveraging variables like medical background, lifestyle choices, and genetic inclinations. Through the identification of risk elements and prognostication of potential outcomes, healthcare providers can intervene proactively, potentially forestalling the onset or reducing the impact of heart diseases [1]. Within this framework, Leveraging predictive model methodologies, notably machine learning algorithms, has garnered significant Cognitive workload management for healthcare domain. These algorithms possess the capability to scrutinize extensive datasets comprising patient data, uncovering intricate patterns and ARTICLE INFO ABSTRACTThe paper delves into the application of various Machine Learning (ML) algorithms for the early identification and prediction of heart diseases. It examines the effectiveness of these algorithms in analyzing diverse datasets related to cardiac health, including medical history, lifestyle factors, and diagnostic tests results. By leveraging ML techniques such as Decision Trees, Support Vector Machines, and Neural Networks, researchers aim to develop robust predictive models capable of identifying individuals at risk of heart conditions with high accuracy. Additionally, the paper discusses the challenges associated with data collection, preprocessing, and model validation in the context of Heart Disease prediction, highlighting the need for further research and innovation in this critical area of healthcare. By scrutinizing datasets comprising patient data and clinical records, our objective is to construct resilient predictive models. Through meticulous evaluation and comparison of different algorithms, our study endeavors to identify the most efficient approaches for precise prediction. Ultimately, our research endeavors to facilitate proactive interventions and tailored healthcare interventions to mitigate the impact of heart diseases more efficiently.

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“…For early heart disease prediction, Barfungpa et al [ 12 ] developed an intelligent system using hybrid deep dense Aquila network. The primary goal of the suggested architecture is to offer a deep learning (DL) model that is coupled with cutting-edge data mining techniques for formulating sensible decisions and precise disease prediction.…”

Section: Related Work On Classical Ao and Its Improved Variantsmentioning

confidence: 99%

A Comprehensive Survey on Aquila Optimizer

Sasmal

Hussien

Das

et al. 2023

Arch Computat Methods Eng

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Aquila Optimizer (AO) is a well-known nature-inspired optimization algorithm (NIOA) that was created in 2021 based on the prey grabbing behavior of Aquila. AO is a population-based NIOA that has demonstrated its effectiveness in the field of complex and nonlinear optimization in a short period of time. As a result, the purpose of this study is to provide an updated survey on the topic. This survey accurately reports on the designed enhanced AO variations and their applications. In order to properly assess AO, a rigorous comparison between AO and its peer NIOAs is conducted over mathematical benchmark functions. The experimental results show the AO provides competitive outcomes.

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An intelligent heart disease prediction system using hybrid deep dense Aquila network

Cited by 15 publications

References 37 publications

PSO-XnB: a proposed model for predicting hospital stay of CAD patients

PSO-XnB: a proposed model for predicting hospital stay of CAD patients

Prediction Of Cardiovascular Disorders Using Machine Learning

A Comprehensive Survey on Aquila Optimizer

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