Exploring The Efficiency of Metaheuristics in Optimal Hyperparameter Tuning for Ensemble Models on Varied Data Modalities DOI

Vivek BC

EAI endorsed transactions on intelligent systems and machine learning applications., Journal Year: 2024, Volume and Issue: 1

Published: Aug. 6, 2024

Effective disease detection systems play an important role in healthcare by supporting diagnosis and treatment. This study provides a comparison of hyperparameter tuning methods for using four health datasets; kidney disease, diabetes detection, heart breast cancer detection. The main objective this research is to prepare datasets normalizing the input testing machine learning models such as Naive Bayes Support Vector Machine (SVM), Logistic Regression k Nearest Neighbor (kNN). identify effective each data set. After implementing models, we apply three techniques: Grid search, random particle ensemble optimization (PSO). These are used tune model parameters. Improve overall performance metrics. evaluation focuses on accuracy measurements compare before after tuning. results illustrate how different techniques can improve across range datasets. By conducting analysis, determine appropriate method set, yielding valuable insights, develop accurate system .These discoveries serve advance field analytics deliver outcomes patients services.

Language: Английский

A Review of Machine Learning’s Role in Cardiovascular Disease Prediction: Recent Advances and Future Challenges DOI Creative Commons
Marwah Abdulrazzaq Naser, Aso Ahmed Majeed, Muntadher Alsabah

et al.

Algorithms, Journal Year: 2024, Volume and Issue: 17(2), P. 78 - 78

Published: Feb. 13, 2024

Cardiovascular disease is the leading cause of global mortality and responsible for millions deaths annually. The rate overall consequences cardiac can be reduced with early detection. However, conventional diagnostic methods encounter various challenges, including delayed treatment misdiagnoses, which impede course raise healthcare costs. application artificial intelligence (AI) techniques, especially machine learning (ML) algorithms, offers a promising pathway to address these challenges. This paper emphasizes central role in health focuses on precise cardiovascular prediction. In particular, this driven by urgent need fully utilize potential enhance light continued progress growing public implications disease, aims offer comprehensive analysis topic. review encompasses wide range topics, types significance learning, feature selection, evaluation models, data collection & preprocessing, metrics prediction, recent trends suggestion future works. addition, holistic view learning’s prediction health. We believe that our will contribute significantly existing body knowledge essential area.

Language: Английский

Citations

17
Analyzing the impact of feature selection methods on machine learning algorithms for heart disease prediction DOI Creative Commons

Zeinab Noroozi,

Azam Orooji, Leila Erfannia

et al.

Scientific Reports, Journal Year: 2023, Volume and Issue: 13(1)

Published: Dec. 18, 2023

Abstract The present study examines the role of feature selection methods in optimizing machine learning algorithms for predicting heart disease. Cleveland Heart disease dataset with sixteen techniques three categories filter, wrapper, and evolutionary were used. Then seven Bayes net, Naïve (BN), multivariate linear model (MLM), Support Vector Machine (SVM), logit boost, j48, Random Forest applied to identify best models prediction. Precision, F-measure, Specificity, Accuracy, Sensitivity, ROC area, PRC measured compare methods' effect on prediction algorithms. results demonstrate that resulted significant improvements performance some (e.g., j48), whereas it led a decrease other (e.g. MLP, RF). SVM-based filtering have best-fit accuracy 85.5. In fact, best-case scenario, result + 2.3 accuracy. SVM-CFS/information gain/Symmetrical uncertainty highest improvement this index. filter number features selected outperformed terms models' ACC, F-measures. However, wrapper-based improved from sensitivity specificity points view.

Language: Английский

Citations

43
A comparative analysis of feature selection models for spatial analysis of floods using hybrid metaheuristic and machine learning models DOI

Javeria Sarwar,

Saud Khan, Muhammad Azmat

et al.

Environmental Science and Pollution Research, Journal Year: 2024, Volume and Issue: 31(23), P. 33495 - 33514

Published: April 29, 2024

Language: Английский

Citations

12
Application of machine learning algorithms and feature selection methods for better prediction of sludge production in a real advanced biological wastewater treatment plant DOI
Ekin Ekıncı, Bilge Özbay, Sevinç İlhan Omurca

et al.

Journal of Environmental Management, Journal Year: 2023, Volume and Issue: 348, P. 119448 - 119448

Published: Nov. 6, 2023

Language: Английский

Citations

18
Empirical exploration of whale optimisation algorithm for heart disease prediction DOI Creative Commons
Stephen Akatore Atimbire, Justice Kwame Appati, Ebenezer Owusu

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Feb. 24, 2024

Abstract Heart Diseases have the highest mortality worldwide, necessitating precise predictive models for early risk assessment. Much existing research has focused on improving model accuracy with single datasets, often neglecting need comprehensive evaluation metrics and utilization of different datasets in same domain (heart disease). This introduces a heart disease prediction approach by harnessing whale optimization algorithm (WOA) feature selection implementing framework. The study leverages five distinct including combined dataset comprising Cleveland, Long Beach VA, Switzerland, Hungarian datasets. others are Z-AlizadehSani, Framingham, South African, Cleveland WOA-guided identifies optimal features, subsequently integrated into ten classification models. Comprehensive reveals significant improvements across critical performance metrics, accuracy, precision, recall, F1 score, area under receiver operating characteristic curve. These enhancements consistently outperform state-of-the-art methods using dataset, validating effectiveness our methodology. framework provides robust assessment model’s adaptability, underscoring WOA’s identifying features multiple domain.

Language: Английский

Citations

8
A novel compressive strength estimation approach for 3D printed fiber-reinforced concrete: integrating machine learning and gene expression programming DOI
Md Nasir Uddin, Junhong Ye, M. Aminul Haque

et al.

Multiscale and Multidisciplinary Modeling Experiments and Design, Journal Year: 2024, Volume and Issue: 7(5), P. 4889 - 4910

Published: April 15, 2024

Language: Английский

Citations

5
Special Issue “Algorithms for Feature Selection (2nd Edition)” DOI Creative Commons
Muhammad Adnan Khan

Algorithms, Journal Year: 2025, Volume and Issue: 18(1), P. 16 - 16

Published: Jan. 3, 2025

This Special Issue focuses on advancing research algorithms, with a particular emphasis feature selection techniques [...]

Language: Английский

Citations

0
Exploration and comparison of the effectiveness of swarm intelligence algorithm in early identification of cardiovascular disease DOI Creative Commons

Tiantian Bai,

Mengru Xu,

Taotao Zhang

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Feb. 7, 2025

Due to the aging of global population and lifestyle changes, cardiovascular disease has become leading cause death worldwide, causing serious public health problems economic pressures. Early accurate prediction is crucial reducing morbidity mortality, but traditional methods often lack robustness. This study focuses on integrating swarm intelligence feature selection algorithms (including whale optimization algorithm, cuckoo search flower pollination Harris hawk particle genetic algorithm) with machine learning technology improve early diagnosis disease. systematically evaluated performance each algorithm under different sizes, specifically by comparing their average running time objective function values identify optimal subset. Subsequently, selected subsets were integrated into ten classification models, a comprehensive weighted evaluation was performed based accuracy, precision, recall, F1 score, AUC value model determine configuration. The results showed that random forest, extreme gradient boosting, adaptive boosting k-nearest neighbor models best combined dataset (weighted score 1), where set consisted 9 key features when size 25; while Framingham dataset, 0.92), its derived from 10 50. this show can effectively screen informative sets, significantly provide strong support for diseases.

Language: Английский

Citations

0
Enhancing Heart Disease Diagnosis with Meta-Heuristic Algorithms: A Combined HHO and PSO Approach DOI
Farzana Begum,

J. Arul Valan

Deleted Journal, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 21, 2025

Language: Английский

Citations

0
Enhanced Cardiovascular Disease Prediction Through a Semi-supervised Grey Ensemble Model DOI
Annwesha Banerjee Majumder, Somsubhra Gupta, Dharmpal Singh

et al.

Lecture notes in business information processing, Journal Year: 2025, Volume and Issue: unknown, P. 218 - 231

Published: Jan. 1, 2025

Language: Английский

Citations

0