Heart disease prediction using IoT based framework and improved deep learning approach: Medical application

Rajkumar, G.; Devi, T. Gayathri; Srinivasan, A.

doi:10.1016/j.medengphy.2022.103937

Cited by 20 publications

(5 citation statements)

References 15 publications

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“…The formulation of the individual activation probability, p(v p ¼ 1jh) is provided in Eqs ( 16) and (17).…”

Section: Hybrid Deep Belief Network and Xgboost Methodsmentioning

confidence: 99%

“…Yaqoob et al ( 16 ) presented a unique hybrid framework addressing both privacy concerns and communication costs, improving prediction accuracy by 1.5%. Rajkumar et al ( 17 ) ventured into IoT-based heart disease prediction using deep learning, marking 98.01% accuracy. Kiran et al ( 18 ) specifically explored the effectiveness of machine learning classifiers for prediction CVD, proposing the GBDT-BSHO approach and achieving 97.89% accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Objective artificial bee colony optimized hybrid deep belief network and XGBoost algorithm for heart disease prediction

Kalita,

Ganesh,

Jayalakshmi

et al. 2023

Front. Digit. Health

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The global rise in heart disease necessitates precise prediction tools to assess individual risk levels. This paper introduces a novel Multi-Objective Artificial Bee Colony Optimized Hybrid Deep Belief Network and XGBoost (HDBN-XG) algorithm, enhancing coronary heart disease prediction accuracy. Key physiological data, including Electrocardiogram (ECG) readings and blood volume measurements, are analyzed. The HDBN-XG algorithm assesses data quality, normalizes using z-score values, extracts features via the Computational Rough Set method, and constructs feature subsets using the Multi-Objective Artificial Bee Colony approach. Our findings indicate that the HDBN-XG algorithm achieves an accuracy of 99%, precision of 95%, specificity of 98%, sensitivity of 97%, and F1-measure of 96%, outperforming existing classifiers. This paper contributes to predictive analytics by offering a data-driven approach to healthcare, providing insights to mitigate the global impact of coronary heart disease.

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“…The formulation of the individual activation probability, p(v p ¼ 1jh) is provided in Eqs ( 16) and (17).…”

Section: Hybrid Deep Belief Network and Xgboost Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-Objective artificial bee colony optimized hybrid deep belief network and XGBoost algorithm for heart disease prediction

Kalita,

Ganesh,

Jayalakshmi

et al. 2023

Front. Digit. Health

View full text Add to dashboard Cite

show abstract

“…The primary causes of the 85% total deaths are heart attacks and strokes. They cause negative effects on human health, resulting in obesity, overweight, high cholesterol, and diabetes ( Gárate-Escamila, El Hassani & Andrès, 2020 ; Rajkumar, Devi & Srinivasan, 2022 ). Often, the indications of aging are puzzling, posing a challenge for practitioners when diagnosing.…”

Section: Introductionmentioning

confidence: 99%

Performance discrepancy mitigation in heart disease prediction for multisensory inter-datasets

Hasan,

Sahid,

Uddin

et al. 2024

PeerJ Computer Science

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Heart disease is one of the primary causes of morbidity and death worldwide. Millions of people have had heart attacks every year, and only early-stage predictions can help to reduce the number. Researchers are working on designing and developing early-stage prediction systems using different advanced technologies, and machine learning (ML) is one of them. Almost all existing ML-based works consider the same dataset (intra-dataset) for the training and validation of their method. In particular, they do not consider inter-dataset performance checks, where different datasets are used in the training and testing phases. In inter-dataset setup, existing ML models show a poor performance named the inter-dataset discrepancy problem. This work focuses on mitigating the inter-dataset discrepancy problem by considering five available heart disease datasets and their combined form. All potential training and testing mode combinations are systematically executed to assess discrepancies before and after applying the proposed methods. Imbalance data handling using SMOTE-Tomek, feature selection using random forest (RF), and feature extraction using principle component analysis (PCA) with a long preprocessing pipeline are used to mitigate the inter-dataset discrepancy problem. The preprocessing pipeline builds on missing value handling using RF regression, log transformation, outlier removal, normalization, and data balancing that convert the datasets to more ML-centric. Support vector machine, K-nearest neighbors, decision tree, RF, eXtreme Gradient Boosting, Gaussian naive Bayes, logistic regression, and multilayer perceptron are used as classifiers. Experimental results show that feature selection and classification using RF produce better results than other combination strategies in both single- and inter-dataset setups. In certain configurations of individual datasets, RF demonstrates 100% accuracy and 96% accuracy during the feature selection phase in an inter-dataset setup, exhibiting commendable precision, recall, F1 score, specificity, and AUC score. The results indicate that an effective preprocessing technique has the potential to improve the performance of the ML model without necessitating the development of intricate prediction models. Addressing inter-dataset discrepancies introduces a novel research avenue, enabling the amalgamation of identical features from various datasets to construct a comprehensive global dataset within a specific domain.

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“…A metamodel with 88% accuracy beats other ML models. Rajkumar et al [6] used the Hungarian heart disease dataset from IoT sensor devices to offer an upgraded deep learning framework for heart disease prediction. The dataset was preprocessed using median studentized residual and feature selected using harris hawk optimization (HHO).…”

Section: Introductionmentioning

confidence: 99%

Heart disease prediction using ML through enhanced feature engineering with association and correlation analysis

Lakshmanarao,

Sai Krishna,

Ravi Kiran

et al. 2024

IJEECS

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Heart disease remains a prevalent and critical health concern globally. This paper addresses the critical task of heart disease prediction through the utilization of advanced machine learning techniques. Our approach focuses on the enhancement of feature engineering by incorporating a novel integration of association and correlation analyses. A heart disease dataset from Kaggle was used for the experiments. Association analysis was applied to the categorical and binary features in the dataset. Correlation analysis was applied to the numerical features in the dataset. Based on the insights from association analysis and correlation analysis, a new dataset was created with combinations of features. Later, newly created features are integrated with the original dataset, and classification algorithms are applied. Five machine learning (ML) classifiers, namely decision tree, k-nearest neighbors (KNN), random forest, XG-Boost, and support vector machine (SVM), were applied to the final dataset and achieved a good accuracy rate for heart disease detection. By systematically exploring associations and relationships with categorical, binary, and numerical features, this paper unveils innovative insights that contribute to a more comprehensive understanding of the heart disease dataset.

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Heart disease prediction using IoT based framework and improved deep learning approach: Medical application

Cited by 20 publications

References 15 publications

Multi-Objective artificial bee colony optimized hybrid deep belief network and XGBoost algorithm for heart disease prediction

Multi-Objective artificial bee colony optimized hybrid deep belief network and XGBoost algorithm for heart disease prediction

Performance discrepancy mitigation in heart disease prediction for multisensory inter-datasets

Heart disease prediction using ML through enhanced feature engineering with association and correlation analysis

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