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

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EBHOA-EMobileNetV2: a hybrid system based on efficient feature selection and classification for cardiovascular disease diagnosis

Computer Methods in Biomechanics & Biomedical Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 23

Published: Feb. 19, 2025

The accurate prediction of cardiovascular disease (CVD) or heart is an essential and challenging task to treat a patient efficiently before occurring attack. Many deep learning machine frameworks have been developed recently predict in intelligent healthcare. However, lack data-recognized appropriate methodologies meant that most existing strategies failed improve accuracy. This paper presents healthcare framework based on model detect disease, motivated by present issues. Initially, the proposed system compiles data from multiple publicly accessible sources. To quality dataset, effective pre-processing techniques are used including (i) interquartile range (IQR) method identify eliminate outliers; (ii) standardization technique handle missing values; (iii) 'K-Means SMOTE' oversampling address issue class imbalance. Using Enhanced Binary Grasshopper Optimization Algorithm (EBHOA), dataset's features chosen. Finally, presence absence CVD predicted using MobileNetV2 (EMobileNetV2) model. Training evaluation approach were conducted UCI Heart Disease Framingham Study datasets. We obtained excellent results comparing with recent methods. beats current approaches concerning performance metrics, according experimental results. For research achieves higher accuracy 98.78%, precision 99%, recall 99% F1 score 99%. 99.39%, 99.50%, learning-based classification combined feature selection yielded best innovative has potential enhance consistency prediction, which would be advantageous for clinical practice care.

Language: Английский

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Evaluating Binary Classifiers for Cardiovascular Disease Prediction: Enhancing Early Diagnostic Capabilities

Journal of Cardiovascular Development and Disease, Journal Year: 2024, Volume and Issue: 11(12), P. 396 - 396

Published: Dec. 9, 2024

Cardiovascular disease (CVD) is a significant global health concern and the leading cause of death in many countries. Early detection diagnosis CVD can significantly reduce risk complications mortality. Machine learning methods, particularly classification algorithms, have demonstrated their potential to accurately predict cardiovascular by analyzing patient data. This study evaluates seven binary including Random Forests, Logistic Regression, Naive Bayes, K-Nearest Neighbors (kNN), Support Vector Machines, Gradient Boosting, Artificial Neural Networks, understand effectiveness predicting CVD. Advanced preprocessing techniques, such as SMOTE-ENN for addressing class imbalance hyperparameter optimization through Grid Search Cross-Validation, were applied enhance reliability performance these models. Standard evaluation metrics, accuracy, precision, recall, F1-score, Area Under Receiver Operating Characteristic Curve (ROC-AUC), used assess predictive capabilities. The results show that kNN achieved highest accuracy (99%) AUC (0.99), surpassing traditional models like Regression Boosting. examines challenges encountered when working with datasets related diseases, feature selection. It demonstrates how issues enhances applicability These findings emphasize reliable tool early prediction, offering improvements over previous studies. research highlights value advanced machine techniques healthcare, key laying foundation future studies aimed at improving prevention.

Language: Английский

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Advancement of Advanced Driver Assistance System in Automobiles through IoT implementation and integration

2022 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI), Journal Year: 2024, Volume and Issue: unknown, P. 1 - 9

Published: May 9, 2024

Language: Английский

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