ArticleJournal of personalized medicine2022
MLP-PSO Hybrid Algorithm for Heart Disease Prediction.
Article in Journal of personalized medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed, 142 citations in OpenAlex.
- Cross-cohort Generalization for Heart Disease Prediction with Explainable AI.Journal of cardiovascular translational research · 2026Article
- Optimized Feature Selection and Deep Neural Networks to Improve Heart Disease Prediction.Journal of imaging informatics in medicine · 2026Article
- A Sensor-Oriented Multimodal Medical Data Acquisition and Modeling Framework for Tumor Grading and Treatment Response Analysis.Sensors (Basel, Switzerland) · 2026Article
- A modular deep learning architecture for interpretable disease prediction across tabular clinical and biometric datasets.PloS one · 2026Article
- A comprehensive survey of artificial intelligence methods for cardiovascular disease detection: Recent advances and future challenges.MethodsX · 2025Review
- Hybrid Wavelet-ML models for regional drought forecasting in Norway.Scientific reports · 2025Article
- Machine learning enhanced expert system for detecting heart failure decompensation using patient reported vitals and electronic health records.Scientific reports · 2025Article
- SGO enhanced random forest and extreme gradient boosting framework for heart disease prediction.Scientific reports · 2025Article
- Revolutionizing Utility of Big Data Analytics in Personalized Cardiovascular Healthcare.Bioengineering (Basel, Switzerland) · 2025Review
- Artificial Intelligence and Non-Destructive Testing Data to Assess Concrete Sustainability of Civil Engineering Infrastructures.Materials (Basel, Switzerland) · 2025Article
- Article
- Leveraging PSO-MLP for intelligent assessment of student learning in remote environments: a multimodal approach.PeerJ. Computer science · 2025Article
- Triboostcardio ensemble model for cardiovascular disease detection using advanced blockchain-enabled health monitoring.Frontiers in artificial intelligence · 2025Article
- From data to decision: an interpretable machine learning model for optimizing RAI therapy in Graves' hyperthyroidism.Frontiers in endocrinology · 2025Article
- Integrating EPSOSA-BP neural network algorithm for enhanced accuracy and robustness in optimizing coronary artery disease prediction.Scientific reports · 2024Article
- Predicting hospitalization costs for pulmonary tuberculosis patients based on machine learning.BMC infectious diseases · 2024Article
- ML-Based Detection of DDoS Attacks Using Evolutionary Algorithms Optimization.Sensors (Basel, Switzerland) · 2024Article
- An Improved Long Short-Term Memory Algorithm for Cardiovascular Disease Prediction.Diagnostics (Basel, Switzerland) · 2024Article
- CSA-DE-LR: enhancing cardiovascular disease diagnosis with a novel hybrid machine learning approach.PeerJ. Computer science · 2024Article
- Analyzing the impact of feature selection methods on machine learning algorithms for heart disease prediction.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMachine Learning (ML) is becoming increasingly popular in healthcare, particularly for improving the timing and accuracy of diagnosis. ML can provide disease prediction by analyzing vast amounts of healthcare data, thereby, empowering patients and healthcare providers with information to make informed decisions about disease prevention. Due to the rising cost of treatment, one of the most important topics in clinical data analysis is the prediction and prevention of cardiovascular disease. It is difficult to manually calculate the chances of developing heart disease due to a myriad of contributing factors.
objectiveThe aim of this paper is to develop and compare various intelligent systems built with ML algorithms for predicting whether a person is likely to develop heart disease using the publicly available Cleveland Heart Disease dataset. This paper describes an alternative multilayer perceptron (MLP) training technique that utilizes a particle swarm optimization (PSO) algorithm for heart disease detection.
methodsThe proposed MLP-PSO hybrid algorithm and ten different ML algorithms are used in this study to predict heart disease. Various classification metrics are used to evaluate the performance of the algorithms.
resultsThe proposed MLP-PSO outperforms all other algorithms, obtaining an accuracy of 84.61%.
conclusionsAccording to our findings, the current MLP-PSO classifier enables practitioners to diagnose heart disease earlier, more accurately, and more effectively.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.