A metaheuristic-enabled training system for ensemble classification technique for heart disease prediction

Sheeba, Paul T; Roy, Deepjyoti; Syed, Mohammad Haider

doi:10.1016/j.advengsoft.2022.103297

Cited by 9 publications

(2 citation statements)

References 33 publications

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“…Furthermore, considering the rapid advancements in medical technology and the increasing availability of health-related data, the application of metaheuristic algorithms in CVD risk assessment is expected to evolve. This evolution includes the incorporation of deep learning and hybrid models that combine the strengths of different metaheuristic techniques to enhance prediction accuracy and robustness [19].…”

Section: Introductionmentioning

confidence: 99%

Fine-Tuned Cardiovascular Risk Assessment: Locally Weighted Salp Swarm Algorithm in Global Optimization

Mohammed,

Hussein,

Haddani

et al. 2024

Mathematics

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The Salp Swarm Algorithm (SSA) is a bio-inspired metaheuristic optimization technique that mimics the collective behavior of Salp chains hunting for food in the ocean. While it demonstrates competitive performance on benchmark problems, the SSA faces challenges with slow convergence and getting trapped in local optima like many population-based algorithms. To address these limitations, this study proposes the locally weighted Salp Swarm Algorithm (LWSSA), which combines two mechanisms into the standard SSA framework. First, a locally weighted approach is introduced and integrated into the SSA to guide the search toward locally promising regions. This heuristic iteratively probes high-quality solutions in the neighborhood and refines the current position. Second, a mutation operator generates new positions for Salp followers to increase randomness throughout the search. In order to assess its effectiveness, the proposed approach was evaluated against the state-of-the-art metaheuristics using standard test functions from the IEEE CEC 2021 and IEEE CEC 2017 competitions. The methodology is also applied to a risk assessment of cardiovascular disease (CVD). Seven optimization strategies of the extreme gradient boosting (XGBoost) classifier are evaluated and compared to the proposed LWSSA-XGBoost model. The proposed LWSSA-XGBoost achieves superior prediction performance with 94% F1 score, 94% recall, 93% accuracy, and 93% area under the ROC curve in comparison with state-of-the-art competitors. Overall, the experimental results demonstrate that the LWSSA enhances SSA’s optimization ability and XGBoost predictive power in automated CVD risk assessment.

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Section: Introductionmentioning

confidence: 99%

Fine-Tuned Cardiovascular Risk Assessment: Locally Weighted Salp Swarm Algorithm in Global Optimization

Mohammed,

Hussein,

Haddani

et al. 2024

Mathematics

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“…Here, FCM-based WOA is employed for selecting best feature. A selected feature subset is evaluated using FCM [22][23][24][25][26] while WOA is employed to determine the best subset feature to maximize accuracy. WOA involves pointing the whale in the direction of the best possible answer from any point in space.…”

mentioning

confidence: 99%

Whale optimization approach for early heart disease prediction based on FCM using DCNN

Aswathi,

Kumar,

Ramakrishnan

2023

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Heart disease is the leading cause of death worldwide. It has an impact on not only the health of patients but also the economies and expenses of the countries. Numerous machine learning and data mining approaches are being developed and explored currently in order to predict various diseases. This paper aims to address the pressing global issue of heart disease by leveraging machine learning and data mining techniques. Specifically, it focuses on utilizing a Fuzzy C means (FCM) approach for attribute segmentation, employing the Whale Optimization Algorithm (WOA) for feature selection, and utilizing Deep Convolutional Neural Networks (DCNNs) for medical diagnosis and early prediction. In this study, the initial stage involves segmenting patient records' attributes using the FCM method. Subsequently, high-ranking features are selected through the WOA algorithm. These segmented features are then input into DCNNs to construct a robust medical diagnosis system and enable early-stage prediction. The DCNNs autonomously extract crucial features without human intervention, enhancing the accuracy of disease prediction. The performance evaluation of the proposed classifier is conducted using the Python platform, with the DCNN achieving an impressive accuracy level of 90.12% during testing. This indicates the DCNN's capability to accurately predict the presence or absence of cardiac disease, showcasing its potential as an effective tool in healthcare. The integration of FCM attribute segmentation, WOA feature selection, and DCNN-based prediction holds significant practical implications. It offers healthcare professionals a valuable tool for diagnosing and predicting heart disease early, potentially saving lives.

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Comparative Analysis of Heart Disease Prediction Using Machine Learning Algorithms

Choudhary,

Tyagi,

Goyal

et al. 2023

Algorithms for Intelligent Systems

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A metaheuristic-enabled training system for ensemble classification technique for heart disease prediction

Cited by 9 publications

References 33 publications

Fine-Tuned Cardiovascular Risk Assessment: Locally Weighted Salp Swarm Algorithm in Global Optimization

Fine-Tuned Cardiovascular Risk Assessment: Locally Weighted Salp Swarm Algorithm in Global Optimization

Whale optimization approach for early heart disease prediction based on FCM using DCNN

Comparative Analysis of Heart Disease Prediction Using Machine Learning Algorithms

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