Effective Heart Disease Prediction Using Machine Learning Techniques

Algorithms, Год журнала: 2023, Номер 16(2), С. 88 - 88

Опубликована: Фев. 6, 2023

The diagnosis and prognosis of cardiovascular disease are crucial medical tasks to ensure correct classification, which helps cardiologists provide proper treatment the patient. Machine learning applications in niche have increased as they can recognize patterns from data. Using machine classify occurrence help diagnosticians reduce misdiagnosis. This research develops a model that correctly predict diseases fatality caused by diseases. paper proposes method k-modes clustering with Huang starting improve classification accuracy. Models such random forest (RF), decision tree classifier (DT), multilayer perceptron (MP), XGBoost (XGB) used. GridSearchCV was used hypertune parameters applied optimize result. proposed is real-world dataset 70,000 instances Kaggle. were trained on data split 80:20 achieved accuracy follows: tree: 86.37% (with cross-validation) 86.53% (without cross-validation), XGBoost: 86.87% 87.02% forest: 87.05% 86.92% perceptron: 87.28% 86.94% cross-validation). models AUC (area under curve) values: 0.94, 0.95, 0.95. conclusion drawn this underlying cross-validation has outperformed all other algorithms terms It highest 87.28%.

Язык: Английский

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Heart Disease Prediction using Effective Machine Learning Techniques

International Journal For Multidisciplinary Research, Год журнала: 2024, Номер 6(2)

Опубликована: Март 13, 2024

In today’s era deaths due to heart disease has become a major issue approximately one person dies per minute disease. This is considering both male and female category this ratio may vary according the region also considered for people of age group 25-69. does not indicate that with other will be affected by diseases. problem start in early predict cause challenge nowadays. Here paper, we have discussed various algorithms tools used prediction

Язык: Английский

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Predicting Coronary Heart Disease Using an Improved LightGBM Model: Performance Analysis and Comparison

IEEE Access, Год журнала: 2023, Номер 11, С. 23366 - 23380

Опубликована: Янв. 1, 2023

Coronary heart disease (CHD) is a dangerous condition that cannot be completely cured. Accurate detection of early coronary artery can assist physicians in treating patients. In this study, prediction model called HY_OptGBM was proposed for predicting CHD by using the optimized LightGBM classifier. To optimize classifier, hyperparameters were adjusted. addition, its loss function improved, and trained adjusted hyperparameters. applying most advanced hyperparameter optimization framework (OPTUNA). The improved referred to as focal (FL). evaluated data from Framingham Heart Institute. evaluate performance model, various metrics, including precision, recall, F score, accuracy, MCC, sensitivity, specificity, AUC, used. AUC value 97.9%, which better than other comparative models. results demonstrate rate identification among general population utilizing method. This, turn, could serve mitigate costs associated with medical treatment patients suffering CHD.

Язык: Английский

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Effective Feature Engineering Technique for Heart Disease Prediction With Machine Learning

IEEE Access, Год журнала: 2023, Номер 11, С. 56214 - 56224

Опубликована: Янв. 1, 2023

Heart failure is a chronic disease affecting millions worldwide. An efficient machine learning-based technique needed to predict heart health status early and take necessary actions overcome this worldwide issue. While medication the primary treatment, exercise increasingly recognized as an effective adjunct therapy in managing failure. In study, we developed approach enhance detection based on patient parameter data involving learning. Our study helps improve at its stages save patients' lives. We employed nine algorithms for comparison proposed novel Principal Component Failure (PCHF) feature engineering select most prominent features performance. optimized PCHF mechanism by creating new set innovation achieve highest accuracy scores. The newly created dataset eight best-fit features. conducted extensive experiments assess efficiency of several algorithms. decision tree method outperformed applied learning models other state-of-the-art studies, achieving high score 100%, which admirable. All methods were successfully validated using cross-validation technique. research has significant scientific contributions medical community.

Язык: Английский

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A Comparative Study of Machine Learning classifiers to analyze the precision of Myocardial Infarction prediction

2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC), Год журнала: 2023, Номер unknown, С. 0949 - 0954

Опубликована: Март 8, 2023

In the modern world, heart disease ranks among main causes of death. Smoking, high blood pressure, and cholesterol are three key risk factors for getting one disease, 47% all US people have at least these factors. Prediction myocardial illness is a significant problem in field medical research methodology. coronary infarction prediction hard issue that hospitals clinicians must deal with. The precision plays crucial influence this prediction. response to worry, authors used dataset well-known machine-learning method predict infarction. system detecting cardiac utilizing artificial intelligence machine learning algorithms topic study. Here, we demonstrate how can be determine person's developing also trying exactly which important cause Myocardial disease. study compared six models achieved satisfactory results. were LightGBM, XGBoost, Logistic Regression, Bagging, Support Vector Machine, Decision Tree, their respective accuracies 79.06%, 72.90%, 83.85%, 84.60%, 72.80%, 82.01%. It was found LightGBM model outperformed others. So, from that, take decision performs best Our findings suggested promising future treatment infarction, but further investigation required before it employed commercially, particularly healthcare sector.

Язык: Английский

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Optimizing the light gradient-boosting machine algorithm for an efficient early detection of coronary heart disease

Informatics and Health, Год журнала: 2024, Номер 1(2), С. 70 - 81

Опубликована: Июль 2, 2024

Coronary heart disease (CHD) remains a prominent cause of mortality globally, necessitating early and accurate detection methods. Traditional diagnostic approaches can be invasive, costly, time-consuming, the need for more efficient alternatives. This aimed to optimize Light Gradient-Boosting Machine (LightGBM) algorithm enhance its performance accuracy in CHD, providing reliable, cost-effective, non-invasive tool. The Framingham Heart Study (FHS) dataset publicly available on Kaggle was used this study. Multiple Imputations by Chained Equations (MICE) were applied separately training testing sets handle missing data. Borderline-SMOTE (Synthetic Minority Over-sampling Technique) set balance dataset. LightGBM selected efficiency classification tasks, Bayesian Optimization with Tree-structured Parzen Estimator (TPE) employed fine-tune hyperparameters. optimized model trained evaluated using metrics such as accuracy, precision, AUC-ROC test set, cross-validation ensure robustness generalizability. showed significant improvement CHD detection. baseline dropped values had an 0.8333, sensitivity 0.1081, precision 0.3429, F1 score 0.1644, AUC 0.6875. With MICE imputation, improved 0.9399, 0.6693, 0.9043, 0.7692, 0.9457. combined approach Borderline-SMOTE, TPE achieved 0.9882, 0.9370, 0.9835, 0.9597, 0.9963, indicating highly effective robust model. demonstrated outstanding study's strengths include comprehensive addressing data class imbalance fine-tuning hyperparameters through Optimization. However, there is other datasets generalizability well-established. study provides strong framework detection, improving clinical practice allowing precise dependable diagnostics interventions.

Язык: Английский

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An Improved Long Short-Term Memory Algorithm for Cardiovascular Disease Prediction

Diagnostics, Год журнала: 2024, Номер 14(3), С. 239 - 239

Опубликована: Янв. 23, 2024

Cardiovascular diseases, prevalent as leading health concerns, demand early diagnosis for effective risk prevention. Despite numerous diagnostic models, challenges persist in network configuration and performance degradation, impacting model accuracy. In response, this paper introduces the Optimally Configured Improved Long Short-Term Memory (OCI-LSTM) a robust solution. Leveraging Salp Swarm Algorithm, irrelevant features are systematically eliminated, Genetic Algorithm is employed to optimize LSTM’s configuration. Validation metrics, including accuracy, sensitivity, specificity, F1 score, affirm model’s efficacy. Comparative analysis with Deep Neural Network Belief establishes OCI-LSTM’s superiority, showcasing notable accuracy increase of 97.11%. These advancements position OCI-LSTM promising accurate efficient cardiovascular diseases. Future research could explore real-world implementation further refinement seamless integration into clinical practice.

Язык: Английский

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Improving Cardiovascular Disease Prediction Through Comparative Analysis of Machine Learning Models: A Case Study on Myocardial Infarction

Опубликована: Ноя. 14, 2023

Cardiovascular disease remains a leading cause of mortality in the contemporary world. Its association with smoking, elevated blood pressure, and cholesterol levels underscores significance these risk factors. This study addresses challenge predicting myocardial illness, formidable task medical research. Accurate predictions are pivotal for refining healthcare strategies. investigation conducts comparative analysis six distinct machine learning models: Logistic Regression, Support Vector Machine, Decision Tree, Bagging, XGBoost, LightGBM. The attained outcomes exhibit promise, accuracy rates as follows: Regression (81.00%), Machine (75.01%), XGBoost (92.72%), LightGBM (90.60%), Tree (82.30%), Bagging (83.01%). Notably, emerges top-performing model. These findings underscore its potential to enhance predictive precision coronary infarction. As prevalence cardiovascular factors persists, incorporating advanced techniques holds refine proactive interventions.

Язык: Английский

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An innovative model for predicting coronary heart disease using triglyceride-glucose index: a machine learning-based cohort study

Cardiovascular Diabetology, Год журнала: 2023, Номер 22(1)

Опубликована: Авг. 4, 2023

Abstract Background Various predictive models have been developed for predicting the incidence of coronary heart disease (CHD), but none them has had optimal value. Although these consider diabetes as an important CHD risk factor, they do not insulin resistance or triglyceride (TG). The unsatisfactory performance prediction may be attributed to ignoring factors despite their proven effects on CHD. We decided modify standard through machine learning determine whether triglyceride-glucose index (TyG-index, a logarithmized combination fasting blood sugar (FBS) and TG that demonstrates resistance) functions better than predictor. Methods Two-thousand participants community-based Iranian population, aged 20–74 years, were investigated with mean follow-up 9.9 years (range: 7.6–12.2). association between TyG-index was using multivariate Cox proportional hazard models. By selecting common components previously validated scores, we substituted in All explained terms how affect prediction. CHD-predicting cut-off points calculated. Results 14.5%. Compared lowest quartile TyG-index, fourth fully adjusted ratio 2.32 (confidence interval [CI] 1.16–4.68, p-trend 0.04). A > 8.42 highest negative value TyG-index-based support vector (SVM) performed significantly diabetes-based SVM only more CHD; it most factor after age Conclusion recommend clinical practice identify individuals at developing aid its prevention .

Язык: Английский

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Improving Cardiovascular Disease Prediction through Comparative Analysis of Machine Learning Models

Journal of Computer Science and Technology Studies, Год журнала: 2024, Номер 6(2), С. 62 - 70

Опубликована: Апрель 20, 2024

Cardiovascular diseases, including myocardial infarction, present significant challenges in modern healthcare, necessitating accurate prediction models for early intervention. This study explores the efficacy of machine learning algorithms predicting leveraging a dataset comprising various clinical attributes sourced from patients with heart failure. Six models, Logistic Regression, Support Vector Machine, XGBoost, LightGBM, Decision Tree, and Bagging, are evaluated based on key performance metrics such as accuracy, precision, recall, F1 Score, AUC. The results reveal XGBoost top performer, achieving an accuracy 94.80% AUC 90.0%. LightGBM closely follows 92.50% 92.00%. Regression emerges reliable option 85.0%. underscores potential enhancing infarction prediction, offering valuable insights decision-making healthcare intervention strategies.

Язык: Английский

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