Heart disease prediction using hyper parameter optimization (HPO) tuning

Valarmathi, R.; Sheela, T.

doi:10.1016/j.bspc.2021.103033

Cited by 66 publications

(21 citation statements)

References 45 publications

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“…High performance is aimed at the machine learning concept by adjusting the hyperparameter values of the classi er algorithms. There are many studies [19][20][21][22][23][24][25] in the literature dealing with this issue. In this study, Randomize Search cross-validation and bayesian search cross-validation methods were used for the optimal combination of SVM hyperparameters.…”

Section: Hyperparameter Tuningmentioning

confidence: 99%

Coronary artery disease classification using support vector machines tuned via randomized search cross-validation

Akyol

2023

Preprint

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Coronary artery disease outstands health problem that causes high mortality in the world population. This disease brings with it fateful problems such as heart attack and heart failure in patients with cardiovascular problems. Early diagnosis of coronary artery disease is essential for the timely administration of the right treatment and reduction of mortality. Angiography is the most preferred method for CAD detection. However, the complications and costs of this method have led researchers to forage alternative methods through machine learning algorithms. By developing a machine learning model with high generalization ability, prediction errors can be minimized. Thus, these models could potentially be useful for specialist physicians in the effective detection of coronary artery disease. The main focus of this study is to perform coronary artery disease detection with improved support vector machines. k-fold cross-validation experiments were performed on the Z-Alizadeh Sani dataset to evaluate the performance of the models. According to the results obtained, support vector machines with randomized search cross-validation provided the best performance when compared to other models. 87.102% average accuracy, 91.176% average sensitivity, 90.852% average precision, 76.996% average specificity, and also 8.824% average false negative rate obtained by 5-fold cross-validation competes with the known approaches in the literature.

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Section: Hyperparameter Tuningmentioning

confidence: 99%

Coronary artery disease classification using support vector machines tuned via randomized search cross-validation

Akyol

2023

Preprint

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“…The Randomized Search is a non-exhaustive technique hence reducing the chance of overfitting of the model but may not guarantee an optimal combination of hyperparameters. Furthermore, it is observed that the randomized tunned models are more accurate than conventionally tunned models for the models having a less number of critical hyperparameters (Kang et al, 2022;Valarmathi & Sheela, 2021)- (Bischl et al, 2023). Therefore, authors used hyperparameter tunning technique and pseudo code are representing this optimization process are as follows:…”

Section: Randomized Searchcv Techniquementioning

confidence: 99%

“…Randomized SearchCV technique is an automated hyperparameter tunning technique which assumes that not all hyperparameters are equally important. The advantage of the automated tunning technique is that it searches over distributions of a large set of possible parameter values compared to conventional techniques such as Manual and Grid Search techniques (Rani et al, 2021; Valarmathi & Sheela, 2021). The conventional techniques consider each parameter for tunning purposes which increases the time and system complicity.…”

Section: Computational Intelligence Techniquesmentioning

confidence: 99%

Comprehensive analysis of supervised algorithms for coronary artery heart disease detection

2023

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The mortality rate of cardiovascular disease is increasing at an alarming rate and affects more than 31% of the world's population. The increment in obesity, strain, high blood pressure, concern, high body mass index, depression, high glucose level, and high cholesterol are the key factors that increase the risk of cardiovascular disease. Coronary heart and cerebrovascular diseases cause 80% of cardiovascular diseases with a higher mortality rate, out of which Coronary Artery Heart Disease (CAHD) causes 25% of the fatality. CAHD fatality can be minimized by early diagnosis. However, due to common symptoms of the non‐fatal disease, CAHD is often ignored by the patients and the health care team. In this paper, the authors comprehensively analysed and compared the two supervised Machine Learning models, i.e., Logistic Regression (LR) and XGBoost, for the early diagnosis of CAHD. The models are applied to a benchmark dataset (Statlog heart disease dataset). The model's parameters are also optimized by performing the Random SearchCV hyperparameter tuning. The comprehensive study is performed on both non‐optimized and optimized models as well as compared to the other well‐known existing models. After parametric optimization, both the models exhibit higher accuracy, i.e., LR 87.78% and XGBoost 91.85%, compared to the non‐optimized techniques, i.e., LR = 83.33% and XGBoost = 77.77%.

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“…The choice of hyperparameters is critical to model performance [31][32][33]. BO has proven to be a very effective optimization algorithm for solving machine learning optimization problems.…”

Section: Bayesian Optimization (Bo)mentioning

confidence: 99%

Vibration prediction and analysis of strip rolling mill based on XGBoost and Bayesian optimization

Yang

Lin

Zhang

et al. 2022

Complex Intell. Syst.

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The stable operation of strip rolling mill is the key factor to ensure the stability of product quality. The design capability of existing domestic imported and self-developed strip rolling mills cannot be fully developed, and the frequent occurrence of mill vibration and operation instability problems seriously restrict the equipment capacity and the production of high-end strip products. The vibration prediction analysis method for hot strip mill based on eXtreme gradient boosting (XGBoost) and Bayesian optimization (BO) is proposed. First, an XGBoost prediction model is developed based on a self-built data set to construct a complex functional relationship between process parameters and rolling mill vibration. Second, the important hyperparameters and parameters of XGBoost are optimized using Bayesian optimization algorithm to improve the prediction accuracy, computational efficiency, and stability of the model. Third, a comprehensive comparison is made between the prediction model in this paper and other well-known machine learning benchmark models. Finally, the prediction results of the model are interpreted using the SHapley Additive exPlanations (SHAP) method. The proposed model outperforms existing models in terms of prediction accuracy, computational speed and stability. At the same time, the degree of influence of each feature on rolling mill vibration is also obtained.

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Heart disease prediction using hyper parameter optimization (HPO) tuning

Cited by 66 publications

References 45 publications

Coronary artery disease classification using support vector machines tuned via randomized search cross-validation

Coronary artery disease classification using support vector machines tuned via randomized search cross-validation

Comprehensive analysis of supervised algorithms for coronary artery heart disease detection

Vibration prediction and analysis of strip rolling mill based on XGBoost and Bayesian optimization

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