Heart disease prediction using hybrid optimization enabled deep learning network with spark architecture

Kanchanamala, Pendela; Alphonse, A. Suja; Reddy, P. Vijayapal

doi:10.1016/j.bspc.2023.104707

Cited by 9 publications

(4 citation statements)

References 31 publications

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“…Feature fusion is done using z-score normalization and missing value imputation. Feature fusion is performed using Hellinger distance with deep Q network which achieved 0.93 of accuracy [52]. The long short-term memory (LSTM) and recurrent neural network (RNN) based smart healthcare system achieved 99.9% accuracy in forecasting heart disease.…”

Section: Performance Measuresmentioning

confidence: 99%

A Review of Machine Learning Techniques Used in the Prediction of Heart Disease

Chaithra,

Siddesha,

Aradhya

et al. 2024

RIA

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Heart disease stands as a principal cause of death worldwide, and its early prediction is essential for effective patient management and the reduction of healthcare expenditures. In this context, machine learning (ML) has emerged as a transformative tool in the healthcare sector, demonstrating a profound capability to discern intricate data patterns and furnish accurate prognostic assessments. The application of ML in cardiology is instrumental for risk prediction, early detection, and the customization of treatment protocols. The current study systematically reviews the spectrum of ML approaches applied to the prediction of heart disease, spanning supervised, unsupervised, reinforcement, and transfer learning methodologies. Data from prominent repositories such as Kaggle and the UCI Machine Learning Repository were employed to evaluate the performance of various ML algorithms, with key metrics including accuracy, sensitivity, and the Area Under the Receiver Operating Characteristic Curve (AUC-ROC). Influential predictors, namely age, gender, cholesterol levels, blood pressure, and lifestyle factors, were integral to the development of these predictive models. Particular attention was given to the exploration of ensemble methods and deep learning frameworks, which have shown to augment prediction accuracy beyond that of traditional models. This research delineates essential risk factors associated with heart disease and underscores the significance of predictive analytics in the healthcare landscape. With a focus on heterogeneous datasets and analytical techniques, the review aims to inform public health strategies and contribute to the alleviation of healthcare burdens. The elucidated findings highlight the promise of ML, particularly through the utilization of ensemble and deep learning methods, in the precursory prediction of heart disease. Such advancements enable healthcare professionals to make more informed decisions, adopt preventative interventions, and mitigate the overall impact on healthcare systems. This exhaustive review also synthesizes the efficacy and practicality of various ML algorithms, providing a valuable compendium for future research initiatives and promoting the integration of cutting-edge technologies in the management of cardiac health.

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Section: Performance Measuresmentioning

confidence: 99%

A Review of Machine Learning Techniques Used in the Prediction of Heart Disease

Chaithra,

Siddesha,

Aradhya

et al. 2024

RIA

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“…Recent research has found that BERT's Self-Attention layer tends to capture shallow syntactic features while struggling to extract deeper semantic information. Moreover, adversarial attack experiments have shown that slight input perturbations can deceive BERT into making incorrect judgments, highlighting the model's lack of robustness [3]. In pursuit of performance, it is essential to prioritize model interpretability to construct more trustworthy and secure NLP systems.…”

Section: Issue Of Model Interpretabilitymentioning

confidence: 99%

Optimization Strategies for Deep Learning Models in Natural Language Processing

Yao,

Yuan

2024

JTPES

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Deep learning models have achieved remarkable performance in the field of natural language processing (NLP), but they still face many challenges in practical applications, such as data heterogeneity and complexity, the black-box nature of models, and difficulties in transfer learning across multilingual and cross-domain scenarios. In this paper, corresponding improvement measures are proposed from four perspectives: model structure, loss functions, regularization methods, and optimization strategies, to address these issues. Extensive experiments on three tasks including text classification, named entity recognition, and reading comprehension confirm the feasibility and effectiveness of the proposed optimization solutions. The experimental results demonstrate that introducing innovative mechanisms like Multi-Head Attention and Focal Loss, and judiciously applying techniques such as LayerNorm and AdamW, can significantly improve model performance. Finally, this paper also explores model compression techniques, providing new insights for deploying deep models in resource-constrained scenarios.

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“…The methodology used in this proposed work has several advantages over traditional ECG signal processing and ML [4] approaches. First, it eliminates the need for manual feature extraction, which involves lot of time, requires considerable effort, and prone to errors [5].…”

Section: Introductionmentioning

confidence: 99%

Single and Multi-Lead ECG Heartbeat Classification Using XGBoost on Pre-trained CNN Feature Layer

Kancharla,

2024

Preprint

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Electrocardiogram (ECG) is a common tool used in technology-assisted systems to help spot heart rhythm problems. Doctors rely on it to identify and understand any irregularities in the heart's activity. Therefore, making the process of analyzing ECG heartbeats automatic is crucial for diagnosing heart conditions effectively. This proposed work introduces a groundbreaking approach for analyzing electrocardiogram (ECG) signals, essential for diagnosing heart conditions. It uniquely combines XGBoost, a potent predictive model, with the capabilities of pretrained convolutional neural networks (CNNs) for detecting features in ECG data. Applied to both single and multi-lead ECG datasets, specifically MIT-BIH and PTB-XL, this approach offers a straightforward yet highly effective way to diagnose cardio vascular diseases. Compared with traditional models like Decision Trees, Random Forest, and others, the proposed method significantly outperforms, achieving accuracies of 95.8% for single-lead and 97% for multi-lead signals. This innovation excels in precision and reduces the complexity and computational needs, showcasing a significant leap forward in efficient and accurate ECG classification.

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Heart disease prediction using hybrid optimization enabled deep learning network with spark architecture

Cited by 9 publications

References 31 publications

A Review of Machine Learning Techniques Used in the Prediction of Heart Disease

A Review of Machine Learning Techniques Used in the Prediction of Heart Disease

Optimization Strategies for Deep Learning Models in Natural Language Processing

Single and Multi-Lead ECG Heartbeat Classification Using XGBoost on Pre-trained CNN Feature Layer

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