Heart disease prediction by using novel optimization algorithm: A supervised learning prospective

Patro, Sibo Prasad; Nayak, Gouri Sankar; Padhy, Neelamadhab

doi:10.1016/j.imu.2021.100696

Cited by 58 publications

(14 citation statements)

References 15 publications

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“…The number of publications on this phenomenon decreased from 12 in 2019 to 7 in 2020. CAD presence prediction 23 [25] Heart disease prediction 5 [26] Coronary heart disease prediction 24 [27] Heart disease prediction 6 [28] CHD detection 25 [29] CHD prediction 7 [30] CAD prediction 26 [31] CHD prediction 8 [32] predict coronary heart disease 27 [33] prediction of CHD 9 [16] CHD Prediction based on risk factors 28 [34] classification of coronary artery disease medical data sets [1] Accuracy of ML algorithms for predicting clinical events 29 [35] Prediction of CHD [17] methodology of predicting CHD 30 [36] CAD detection [37] CAD detection 31 [2] CHD Prediction [38] prediction of heart diseases 32 [39] Heart Disease Diagnosis [40] prediction of heart diseases 33 [41] CHD prediction [42] CAD diagnosis 34 [43] CHD prediction [44] Prediction of CHD 35 [45] NN-based prediction of CHD [46] Diagnosing CHD 36 [47] Prediction of CHD [48] prediction of heart disease 37 [49] Prediction of CHD [50] CHD Diagnosis…”

Section: Resultsmentioning

confidence: 99%

Application of Machine Learning Algorithms in Coronary Heart Disease: A Systematic Literature Review and Meta-Analysis

Kutiame¹,

Millham²,

Adekoya³

et al. 2022

IJACSA

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Section: Resultsmentioning

confidence: 99%

Application of Machine Learning Algorithms in Coronary Heart Disease: A Systematic Literature Review and Meta-Analysis

Kutiame¹,

Millham²,

Adekoya³

et al. 2022

IJACSA

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“…Several new research opportunities in healthcare have been enabled by advances in ML and advances in computing capabilities [8]. Various researchers have proposed ML algorithms to enhance the accuracy of disease prediction [9][10][11]. To refine the precision of the outcomes, much of the research has meticulously evaluated the presence of missing data in the dataset, a crucial aspect in the data preprocessing process.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhancing Heart Disease Prediction Accuracy through Machine Learning Techniques and Optimization

Chandrasekhar

Peddakrishna

2023

Processes

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In the medical domain, early identification of cardiovascular issues poses a significant challenge. This study enhances heart disease prediction accuracy using machine learning techniques. Six algorithms (random forest, K-nearest neighbor, logistic regression, Naïve Bayes, gradient boosting, and AdaBoost classifier) are utilized, with datasets from the Cleveland and IEEE Dataport. Optimizing model accuracy, GridsearchCV, and five-fold cross-validation are employed. In the Cleveland dataset, logistic regression surpassed others with 90.16% accuracy, while AdaBoost excelled in the IEEE Dataport dataset, achieving 90% accuracy. A soft voting ensemble classifier combining all six algorithms further enhanced accuracy, resulting in a 93.44% accuracy for the Cleveland dataset and 95% for the IEEE Dataport dataset. This surpassed the performance of the logistic regression and AdaBoost classifiers on both datasets. This study’s novelty lies in the use of GridSearchCV with five-fold cross-validation for hyperparameter optimization, determining the best parameters for the model, and assessing performance using accuracy and negative log loss metrics. This study also examined accuracy loss for each fold to evaluate the model’s performance on both benchmark datasets. The soft voting ensemble classifier approach improved accuracies on both datasets and, when compared to existing heart disease prediction studies, this method notably exceeded their results.

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“…The optimization algorithms boost the accuracy of the disease prediction and especially the nature-inspired algorithms nowadays find more applications. A prediction model [23] using the machine learning classifiers and the Swarm Optimization technique was proposed by Patro et al The authors designed the prediction framework using different classifier algorithms such as Naïve Bayes, Bayesian Optimized Support Vector Machine (BO-SVM), K Nearest Neighbour and Swarm Optimized Neural Network. Among the experimented algorithms, the BO-SVM resulted in high accuracy.…”

Section: Devi Et Al Proposed a Workmentioning

confidence: 99%

Deep Neural Network Based Cardio Vascular Disease Prediction Using Binarized Butterfly Optimization

Amutha¹,

Sekar²

2023

Intelligent Automation &Amp; Soft Computing

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In this digital era, Cardio Vascular Disease (CVD) has become the leading cause of death which has led to the mortality of 17.9 million lives each year. Earlier Diagnosis of the people who are at higher risk of CVDs helps them to receive proper treatment and helps prevent deaths. It becomes inevitable to propose a solution to predict the CVD with high accuracy. A system for predicting Cardio Vascular Disease using Deep Neural Network with Binarized Butterfly Optimization Algorithm (DNN-BBoA) is proposed. The BBoA is incorporated to select the best features. The optimal features are fed to the deep neural network classifier and it improves prediction accuracy and reduces the time complexity. The usage of a deep neural network further helps to improve the prediction accuracy with minimal complexity. The proposed system is tested with two datasets namely the Heart disease dataset from UCI repository and CVD dataset from Kaggle Repository. The proposed work is compared with different machine learning classifiers such as Support Vector Machine, Random Forest, and Decision Tree Classifier. The accuracy of the proposed DNN-BBoA is 99.35% for the heart disease data set from UCI repository yielding an accuracy of 80.98% for Kaggle repository for cardiovascular disease dataset.

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Heart disease prediction by using novel optimization algorithm: A supervised learning prospective

Cited by 58 publications

References 15 publications

Application of Machine Learning Algorithms in Coronary Heart Disease: A Systematic Literature Review and Meta-Analysis

Application of Machine Learning Algorithms in Coronary Heart Disease: A Systematic Literature Review and Meta-Analysis

Enhancing Heart Disease Prediction Accuracy through Machine Learning Techniques and Optimization

Deep Neural Network Based Cardio Vascular Disease Prediction Using Binarized Butterfly Optimization

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