Enhancing Heart Disease Prediction through Ensemble Learning Techniques with Hyperparameter Optimization

Algorithms, Journal Year: 2023, Volume and Issue: 16(6), P. 308 - 308

Published: June 20, 2023

Heart disease is a significant global health issue, contributing to high morbidity and mortality rates. Early accurate heart prediction crucial for effectively preventing managing the condition. However, this remains challenging task achieve. This study proposes machine learning model that leverages various preprocessing steps, hyperparameter optimization techniques, ensemble algorithms predict disease. To evaluate performance of our model, we merged three datasets from Kaggle have similar features, creating comprehensive dataset analysis. By employing extra tree classifier, normalizing data, utilizing grid search cross-validation (CV) optimization, splitting with an 80:20 ratio training testing, proposed approach achieved impressive accuracy 98.15%. These findings demonstrated potential accurately predicting presence or absence Such predictions could significantly aid in early prevention, detection, treatment, ultimately reducing associated

Language: Английский

---

Enhancing heart disease prediction using a self-attention-based transformer model

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

Published: Jan. 4, 2024

Abstract Cardiovascular diseases (CVDs) continue to be the leading cause of more than 17 million mortalities worldwide. The early detection heart failure with high accuracy is crucial for clinical trials and therapy. Patients will categorized into various types disease based on characteristics like blood pressure, cholesterol levels, rate, other characteristics. With use an automatic system, we can provide diagnoses those who are prone by analyzing their In this work, deploy a novel self-attention-based transformer model, that combines self-attention mechanisms networks predict CVD risk. layers capture contextual information generate representations effectively model complex patterns in data. Self-attention interpretability giving each component input sequence certain amount attention weight. This includes adjusting output layers, incorporating modifying processes collect relevant information. also makes it possible physicians comprehend which features data contributed model's predictions. proposed tested Cleveland dataset, benchmark dataset University California Irvine (UCI) machine learning (ML) repository. Comparing several baseline approaches, achieved highest 96.51%. Furthermore, outcomes our experiments demonstrate prediction rate higher cutting-edge approaches used prediction.

Language: Английский

---

A Review of Machine Learning’s Role in Cardiovascular Disease Prediction: Recent Advances and Future Challenges

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

---

THE PREDICTION OF HEART DISEASE USING MACHINE LEARNING ALGORITHMS

Science Journal of University of Zakho, Journal Year: 2024, Volume and Issue: 12(3), P. 285 - 293

Published: July 14, 2024

Heart disease threatens the lives of around one individual per minute, establishing it as foremost cause mortality in contemporary era. A wide range individuals over globe has encountered intricacies associated with cardiovascular illness. Various factors, such hypertension, elevated levels cholesterol, and an irregular pulse rhythm hinder early identification a disease. In cardiology, similar to other branches Medicine, timely precise cardiac diseases is utmost importance. Anticipating onset heart failure at appropriate moment can provide challenges, particularly for cardiologists surgeons. Fortunately, categorisation forecasting models assist medical business real applications data. Regarding this, Machine Learning (ML) algorithms techniques have benefited from automated analysis several datasets complex data aid community diagnosing heart-related diseases. Predicting if patient early-stage primary goal this paper. prior study that worked on Erbil Disease dataset proved Naïve Bayes (NB) got accuracy 65%, which worst classifier, while Decision Tree (DT) obtained highest 98%. article, comparison been applied using same (i.e., dataset) between multiple ML algorithms, instance, LR (Logistic Regression), KNN (K-Nearest Neighbours), SVM (Support Vector Machine), DT (Decision Tree), MLP (Multi-Layer Perceptron), NB (Naïve Bayes) RF (Random Forest). Surprisingly, we 98% after applying LR, MLP, RF, was best outcome. Furthermore, by classifier differed incredibly received work.

Language: Английский

---

Analyzing the impact of feature selection methods on machine learning algorithms for heart disease prediction

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

---

A Novel Predictive Analysis to Identify the Weather Impacts for Congenital Heart Disease Using Reinforcement Learning

Published: Sept. 1, 2023

Reinforcement learning is a powerful approach for predictive analysis to identify the weather impacts congenital heart disease. The key advantages of this method include utilization sensor data predict conditions on daily basis and ability learn from feedback adapt predictions over time. In paper, an innovation model has proposed by using reinforcement algorithm. It can gain important insights regarding impact models developed have potential help medical professionals as well general public in better predicting managing This could reduce costs associated with care ultimately improve health outcomes.

Language: Английский

---

TPTM-HANN-GA: A Novel Hyperparameter Optimization Framework Integrating the Taguchi Method, an Artificial Neural Network, and a Genetic Algorithm for the Precise Prediction of Cardiovascular Disease Risk

Mathematics, Journal Year: 2024, Volume and Issue: 12(9), P. 1303 - 1303

Published: April 25, 2024

The timely and precise prediction of cardiovascular disease (CVD) risk is essential for effective prevention intervention. This study proposes a novel framework that integrates the two-phase Taguchi method (TPTM), hyperparameter artificial neural network (HANN), genetic algorithm (GA) called TPTM-HANN-GA. efficiently optimizes hyperparameters an (ANN) model during training stage, significantly enhancing accuracy risk. proposed TPTM-HANN-GA requires far fewer experiments than traditional grid search, making it highly suitable application in resource-constrained, low-power computers, edge intelligence (edge AI) devices. Furthermore, successfully identified optimal configurations ANN model’s hyperparameters, resulting hidden layer 4 nodes, tanh activation function, SGD optimizer, learning rate 0.23425849, momentum 0.75462782, seven nodes. optimized achieves 74.25% predicting disease, which exceeds existing state-of-the-art GA-ANN TSTO-ANN models. enables personalized CVD to be conducted on computers edge-AI devices, achieving goal point-of-care testing (POCT) empowering individuals manage their heart health effectively.

Language: Английский

---

A Literature Review for Detection and Projection of Cardiovascular Disease Using Machine Learning

EAI Endorsed Transactions on Internet of Things, Journal Year: 2024, Volume and Issue: 10

Published: March 7, 2024

The heart is a vital organ that indispensable in ensuring the general health and welfare of individuals. Cardiovascular diseases (CVD) are major concern worldwide leading cause death, leaving behind diabetes cancer. To deal with problem, it essential for early detection prediction CVDs, which can significantly reduce morbidity mortality rates. Computer-aided techniques facilitate physicians diagnosis many disorders, such as valve dysfunction, failure, etc. Living an "information age," every day million bytes data generated, we turn these into knowledge clinical investigation using technique mining. Machine learning algorithms have shown promising results predicting disease based on different risk parameter. In this study, purpose our aim to appraise examine outputs generated by machine including support vector machines, artificial neural network, logistic regression, random forest decision trees.This literature survey highlights correctness forecasting problem be used basis building Clinical decision-making aid detect prevent at stage.

Language: Английский

---

Heart Disease Prediction Using GridSearchCV and Random Forest

EAI Endorsed Transactions on Pervasive Health and Technology, Journal Year: 2024, Volume and Issue: 10

Published: March 22, 2024

INTRODUCTION: This study explores machine learning algorithms (SVM, Adaboost, Logistic Regression, Naive Bayes, and Random Forest) for heart disease prediction, utilizing comprehensive cardiovascular clinical data. Our research enables early detection, aiding timely interventions preventive measures. Hyperparameter tuning via GridSearchCV enhances model accuracy, reducing disease's burdens. Methodology includes preprocessing, feature engineering, training, cross-validation. Results favor Forest promising applications. work advances predictive healthcare analytics, highlighting learning's pivotal role. findings have implications policy, advocating efficient models management. Advanced analytics can save lives, cut costs, elevate care quality. OBJECTIVES: Evaluate the to enable interventions, METHODS: Utilize hyperparameter enhance accuracy. Employ cross-validation methodologies. performance of SVM, algorithms. RESULTS: The reveals as favored algorithm showing promise contribute improved burden disease. CONCLUSION: underscores role in

Language: Английский

---

CARDPSoML: Comparative approach to analyze and predict cardiovascular disease based on medical report data and feature fusion approach

Health Science Reports, Journal Year: 2024, Volume and Issue: 7(1)

Published: Jan. 1, 2024

Diabetes patients are at high risk for cardiovascular disease (CVD), which makes early identification and prompt management essential. To diagnose CVD in diabetic patients, this work attempts to provide a feature-fusion strategy employing supervised learning classifiers.

Language: Английский

---