A Deep Convolutional Neural Network for the Early Detection of Heart Disease

Arooj, Sadia; Rehman, Saif Ur; Imran, Azhar; Almuhaimeed, Abdullah; Alzahrani, A. Khuzaim; Alzahrani, Abdulkareem

doi:10.3390/biomedicines10112796

Cited by 29 publications

(13 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Sequential Minimal Optimization (SMO) classi er using Chi-Squared attribute evaluator was found to have best performance, with an accuracy of 86.47%. The study by Arooj et al showed that the deep learning algorithm could be implemented to predict heart disease [11]. In this study, the model with the CNN algorithm resulted in an accuracy of 91.7%.…”

Section: Introductionmentioning

confidence: 69%

A comparative study of machine learning approaches to heart disease prediction: an empirical analysis

Hidayaturrohman

Clarke

Taflan

et al. 2023

Preprint

View full text Add to dashboard Cite

Purpose: This paper compares five supervised learning algorithms (support vector machines, k-nearest neighbor, decision tree, random forest, and AdaBoost) for predicting heart disease and examines the impact of normalization and GridSearch hyper-parameter tuning on model performance. Methods: The study utilizes the Cleveland database from the University of California-Irvine (UCI) repository, comprising data on 918 instances of heart disease patients with 12 attributes. Eleven attributes serve as predictors, while one attribute represents the target class. Models are built and tested using this dataset. Results: Comparing the algorithm performances with existing literature, accuracies range from 89.13–91.85%. AdaBoost exhibits the highest performance, whereas the decision tree performs the least effectively. Results surpass those reported in the literature. Normalization improves prediction performance by 17% for Support Vector Machines (SVM) and 14% for k-nearest neighbor (kNN). SVM does not benefit from GridSearch, while GridSearch enhances the decision tree and AdaBoost by 7% and 4% respectively. Normalization combined with GridSearch improves kNN and random forest by 2–3%. Conclusion: This study compares supervised learning algorithms for heart disease prediction. AdaBoost emerges as the top-performing algorithm, while the decision tree performs relatively poorly. The findings surpass those in the literature. Normalization significantly improves performance for SVM and kNN, while GridSearch enhances the decision tree and AdaBoost. Combined, normalization and GridSearch yield performance improvements for kNN and random forest. These results contribute to the field of heart disease prediction, offering valuable insights for algorithm selection and guiding future research.

show abstract

Section: Introductionmentioning

confidence: 69%

A comparative study of machine learning approaches to heart disease prediction: an empirical analysis

Hidayaturrohman

Clarke

Taflan

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Attention mechanisms dynamically assigns weights to the features to minimize the effect of less important features. The variants of attention mechanisms are self-attention ( Arooj et al, 2022 ), graph attention ( Wekesa et al, 2020a ), coordinate attention ( Xie C et al, 2022 ), dimensionality reduction attention ( Wang and Wang, 2022 ), residual attention ( Zhao et al, 2022 ), and spatial attention. Self-attention enhances information content by focusing on a single sequence to compute the sequence representation.…”

Section: Multi-omics Data Integration Interpretation and Disease Pred...mentioning

confidence: 99%

A review of multi-omics data integration through deep learning approaches for disease diagnosis, prognosis, and treatment

Wekesa

Kimwele

2023

Front. Genet.

View full text Add to dashboard Cite

Accurate diagnosis is the key to providing prompt and explicit treatment and disease management. The recognized biological method for the molecular diagnosis of infectious pathogens is polymerase chain reaction (PCR). Recently, deep learning approaches are playing a vital role in accurately identifying disease-related genes for diagnosis, prognosis, and treatment. The models reduce the time and cost used by wet-lab experimental procedures. Consequently, sophisticated computational approaches have been developed to facilitate the detection of cancer, a leading cause of death globally, and other complex diseases. In this review, we systematically evaluate the recent trends in multi-omics data analysis based on deep learning techniques and their application in disease prediction. We highlight the current challenges in the field and discuss how advances in deep learning methods and their optimization for application is vital in overcoming them. Ultimately, this review promotes the development of novel deep-learning methodologies for data integration, which is essential for disease detection and treatment.

show abstract

“…A Convolutional Neural Network in [59] achieves 91.7% accuracy over UCI dataset. A deep belief network is implemented in [60] which uses cuckoo search algorithm for performing classification on five different heart disease datasets.…”

Section: Related Workmentioning

confidence: 99%

Increasing the reliability of Heart Disease classification models using Class Balancing techniques with Feature Engineering

Sharma,

Lalwani

2023

Preprint

View full text Add to dashboard Cite

In this article, we explore the natural problem of class imbalance in the heart disease datasets. Aiming for a comprehensive examination of the class balancing techniques we train and test our models on three different datasets all suffering from different degree of class imbalance. The Healthcare dataset (mid size) and the BRFSS-2015 dataset (large dataset) having huge class imbalance, and the Iraq hospital dataset with mild class imbalance. Feature selection is done using backward elimination and Logistic Regression (LR), Adaptive Boosting (ADB), XGBoost (XGB), Random Forest (RF), and Ensemble classifiers are combined with the four popular class balancing techniques namely Random Under Sample, Random Over Sample, SMOTETomek, and ADASYN. As proven by results, Random Under Sampling and Random Over Sampling combined with Logistic Regression and ADA Boost proves the best combination for the highly imbalanced Healthcare Stroke Dataset and BRFSS-2015 dataset by producing almost 15%-20% increase in the in F1-Score of the minority class and AU-ROC making the classifier model more robust but with a minor decline in the overall accuracy. For the mildly imbalanced Iraq Hospital dataset the same combination marginally improves the F1-Score and AU-ROC without compromising on the overall accuracy and the F1-Score of majority class.

show abstract

A Deep Convolutional Neural Network for the Early Detection of Heart Disease

Cited by 29 publications

References 46 publications

A comparative study of machine learning approaches to heart disease prediction: an empirical analysis

A comparative study of machine learning approaches to heart disease prediction: an empirical analysis

A review of multi-omics data integration through deep learning approaches for disease diagnosis, prognosis, and treatment

Increasing the reliability of Heart Disease classification models using Class Balancing techniques with Feature Engineering

Contact Info

Product

Resources

About