A Technical Comparative Heart Disease Prediction Framework Using Boosting Ensemble Techniques

Computation, Год журнала: 2024, Номер 12(1), С. 15 - 15

Опубликована: Янв. 16, 2024

This paper addresses the global surge in heart disease prevalence and its impact on public health, stressing need for accurate predictive models. The timely identification of individuals at risk developing cardiovascular ailments is paramount implementing preventive measures interventions. World Health Organization (WHO) reports that diseases, responsible an alarming 17.9 million annual fatalities, constitute a significant 31% mortality rate. intricate clinical landscape, characterized by inherent variability complex interplay factors, poses challenges accurately diagnosing severity cardiac conditions predicting their progression. Consequently, early emerges as pivotal factor successful treatment heart-related ailments. research presents comprehensive framework prediction leveraging advanced boosting techniques machine learning methodologies, including Cat boost, Random Forest, Gradient boosting, Light GBM, Ada boost. Focusing “Early Heart Disease Prediction using Boosting Techniques”, this aims to contribute development robust models capable reliably forecasting health risks. Model performance rigorously assessed substantial dataset illnesses from UCI library. With 26 feature-based numerical categorical variables, encompasses 8763 samples collected globally. empirical findings highlight AdaBoost preeminent performer, achieving notable accuracy 95% excelling metrics such negative predicted value (0.83), false positive rate (0.04), (0.01). These results underscore AdaBoost’s superiority overall compared alternative algorithms, contributing valuable insights field prediction.

Язык: Английский

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Forecasting Coronary Heart Disease Risk With a 2-Step Hybrid Ensemble Learning Method and Forward Feature Selection Algorithm

IEEE Access, Год журнала: 2023, Номер 11, С. 136758 - 136769

Опубликована: Янв. 1, 2023

Detecting cardiovascular irregularities in a timely manner is crucial for preventing any fatal risks. This research aims to devise an efficient forecasting algorithm the prognosis of Coronary Heart Disease (CHD). The study includes diverse sample individuals from Framingham, Massachusetts, with varying demographic, clinical, and co-morbidity parameters. We aim achieve this two-step ensemble Machine Learning model. Firstly, feature importance integrated conventional classifiers build Feature Weighted Meta-Models Forward selection algorithm. Subsequently, top-performing are combined design Hybrid Voting Models predict risk CHD ten-year timeframe by minimizing misclassification rate. proposed models undergo vetting using multiple metrics, including F1 score, Matthew's Correlation Coefficient (MCC), Misclassification Ratio (MCR), Accuracy. Given high cost associated healthcare domain, these metrics carefully considered. resulting model demonstrated strong predictive capability risk, achieving overall accuracy rate 95.87%. score calculated be 0.91, MCC 0.83, MCR 0.041. Notably, achieved impressive results only seven features, reducing time complexity prediction. In comparison classifiers, our 23.94% improvement accuracy, 17.23% over average Meta-models highlighting its effectiveness predicting risk.

Язык: Английский

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Enhancing Elderly Fall Detection through IoT-Enabled Smart Flooring and AI for Independent Living Sustainability

Sustainability, Год журнала: 2023, Номер 15(22), С. 15695 - 15695

Опубликована: Ноя. 7, 2023

In the realm of sustainable IoT and AI applications for well-being elderly individuals living alone in their homes, falls can have severe consequences. These consequences include post-fall complications extended periods immobility on floor. Researchers been exploring various techniques fall detection over past decade, this study introduces an innovative Elder Fall Detection system that harnesses technologies. our configuration, we integrate RFID tags into smart carpets along with readers to identify among population. To simulate events, conducted experiments 13 participants. these experiments, embedded transmit signals readers, effectively distinguishing from events regular movements. When a is detected, activates green signal, triggers alarm, sends notifications alert caregivers or family members. enhance precision detection, employed machine deep learning classifiers, including Random Forest (RF), XGBoost, Gated Recurrent Units (GRUs), Logistic Regression (LGR), K-Nearest Neighbors (KNN), analyze collected dataset. Results show algorithm achieves 43% accuracy rate, GRUs exhibit 44% XGBoost 33% rate. Remarkably, KNN outperforms others exceptional rate 99%. This research aims propose efficient framework significantly contributes enhancing safety overall independently individuals. It aligns principles sustainability applications.

Язык: Английский

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Enhancing stroke disease classification through machine learning models via a novel voting system by feature selection techniques

PLoS ONE, Год журнала: 2025, Номер 20(1), С. e0312914 - e0312914

Опубликована: Янв. 9, 2025

Heart disease remains a leading cause of mortality and morbidity worldwide, necessitating the development accurate reliable predictive models to facilitate early detection intervention. While state art work has focused on various machine learning approaches for predicting heart disease, but they could not able achieve remarkable accuracy. In response this need, we applied nine algorithms XGBoost, logistic regression, decision tree, random forest, k-nearest neighbors (KNN), support vector (SVM), gaussian naïve bayes (NB gaussian), adaptive boosting, linear regression predict based range physiological indicators. Our approach involved feature selection techniques identify most relevant predictors, aimed at refining enhance both performance interpretability. The were trained, incorporating processes such as grid search hyperparameter tuning, cross-validation minimize overfitting. Additionally, have developed novel voting system with advance classification. Furthermore, evaluated using key metrics including accuracy, precision, recall, F1-score, area under receiver operating characteristic curve (ROC AUC). Among models, XGBoost demonstrated exceptional performance, achieving 99% F1-Score, 98% 100% ROC AUC. This study offers promising diagnosis preventive healthcare.

Язык: Английский

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The Qalbi Paradigm: Redefining Cardiovascular Health through Machine Learning-Based Data-Driven Insights and Service Design

Опубликована: Янв. 1, 2025

This paper presents an innovative and comprehensive approach to understanding managing Cardiovascular Disease (CVD), one of the foremost health challenges globally, responsible for one-third all global deaths. The study harmonizes insights from academic research public discourse offer a holistic view CVD, analysing extensive datasets Scopus X (formerly Twitter) comprising 43,398 article data 670,592 tweets. Through this analysis, not only identifies 32 parameters 12 X, grouped into macro-categories, but also extracts detailed taxonomies, providing nuanced CVD multiple perspectives. A key achievement is development Qalbi framework, groundbreaking model prevention management. Integral framework are nine meticulously designed services, each tailored specific identified in our research. These services embody collaborative interdisciplinary approach, addressing complex interplay biological, psychological, social factors heart health. They range AI-based diagnostic platforms integrative care models that combine conventional alternative treatments. Crucially, these represent significant advancements care, directly gaps existing by offering holistic, patient-centric solutions. showcases framework's flexibility capability develop diverse solutions multifaceted nature cardiovascular Additionally, offers literature review nearly 200 articles on highlighting crucial healthcare policy-making. novelty lies its methodology, combining advanced machine learning with analysis enhance clinical societal perception CVD. emphasizing considering lifestyle environmental factors, contributes significantly policy practice. It design operation can improve efficiency effectiveness management, setting new standards future potentially transforming practices policies. enhances pathways patient engagement initiatives.

Язык: Английский

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Biomedical Informatics: Considering Predictive Models for Early Detection of Heart Diseases

Lecture notes in networks and systems, Год журнала: 2025, Номер unknown, С. 98 - 112

Опубликована: Янв. 1, 2025

Язык: Английский

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A Systematic Review on Machine Learning Intelligent Systems for Heart Disease Diagnosis

Archives of Computational Methods in Engineering, Год журнала: 2025, Номер unknown

Опубликована: Март 15, 2025

Язык: Английский

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TREEMA: an implementation of resilient and transparent consensus learning on Web3

International Journal of Information Technology, Год журнала: 2025, Номер unknown

Опубликована: Март 27, 2025

Язык: Английский

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Comparative Analysis of Machine Learning Models for Heart Disease Prediction

International Journal of Advanced Research in Science Communication and Technology, Год журнала: 2025, Номер unknown, С. 580 - 587

Опубликована: Апрель 7, 2025

The diagnosis and prognosis of cardiovascular disease play a vital role in ensuring accurate classification, which assists cardiologists providing appropriate treatment to patients. adoption machine learning the medical field has grown significantly due its ability detect patterns from data. Leveraging for classification can help reduce risk misdiagnosis. This study presents model designed accurately predict occurrences, ultimately minimizing fatalities. proposed method utilizes k-modes clustering with Huang initialization enhance accuracy. Machine algorithms such as Random Forest (RF), Decision Tree Classifier (DT), Multilayer Perceptron (MP), XGBoost (XGB) are employed. Hyperparameter tuning using GridSearchCV was conducted optimize performance. applied real-world Kaggle dataset comprising 70,000 instances, an 80:20 train-test split.

Язык: Английский

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Prediction of Early Heart Attack Possibility Using Machine Learning

Опубликована: Март 3, 2023

The most vital or important organ in our body is the heart. Over recent decades, cardiac diseases has been primary cause of mortality worldwide. Our heart employed to regulate and sustain blood flow. A data-driven prediction model that takes into consideration risk factors for disease might be quite useful healthcare industry reach an early diagnosis disease. Clinical practitioners academics are very interested developing a reliable method predicting research paper's main focus on those who more prone develop given certain medical criteria, therefore improves treatment lowers costs. We have some machine learning compare accuracy several techniques, it observed Random Forest classifier outperformed with 87.9% comparing other algorithms.

Язык: Английский

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