Long-Term Coronary Artery Disease Risk Prediction with Machine Learning Models

Trigka, Maria; Δρίτσας, Ηλίας

doi:10.3390/s23031193

Cited by 33 publications

(11 citation statements)

References 71 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This study employed a total of six classification algorithms. Trigka et al [33] developed a stacking ensemble model after applying SVM, NB, and KNN with a 10-fold cross-validation synthetic minority oversampling technique (SMOTE) in order to balance out imbalanced datasets. This study demonstrated that a stacking SMOTE with a 10-fold cross-validation achieved an accuracy of 90.9%.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhancing Heart Disease Prediction Accuracy through Machine Learning Techniques and Optimization

Chandrasekhar

Peddakrishna

2023

Processes

View full text Add to dashboard Cite

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.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Enhancing Heart Disease Prediction Accuracy through Machine Learning Techniques and Optimization

Chandrasekhar

Peddakrishna

2023

Processes

View full text Add to dashboard Cite

show abstract

“…In this research work, the author [13] tested a number of machine learning (ML) models after using or not using the Synthetic Minority Oversampling Method (SMOTE), assessing and comparing their accuracy, precision, and recall. The stacking ensemble model using SMOTE with 10-fold cross validation outperformed the other models, with an accuracy of 90.9%.…”

Section: Literature Reviewmentioning

confidence: 99%

Machine Learning based Prediction and Diagnosis of Heart Disease using multiple models

Maurya

Prakash

2023

Preprint

View full text Add to dashboard Cite

Nowadays, heart disease is considered to be the main cause of sickness. Since the majority of people are unaware of their own kind and severity of heart disease, heart disease is now a significant problem that affects people of all ages. On the other hand, manual approach of prediction is challenging and often requires the capability to choose the relevant approach. To resolve these issues, various machine-learning models are playing a vital role in automatic disease prediction in medical field. In this study, we have calculated and made a comparison of accuracy of various machine learning models such as SVM, KNN, Logistic Regression, Decision Tree, Random Forest, Gaussian Naive Bayes, AdaBoost, Extra Tree Classifier and Gradient Boosting for prediction of heart disease using UCI repository dataset for training and testing of models. Among all the models used, the highest accuracy of 95.08% obtained by the Gradient Boosting model The major aim of the paper is to get a reliable, computationally effective machine learning algorithm for heart disease prediction.

show abstract

“…Each student was equipped with a wireless single-channel Neurosky MindSet EEG headset. [26], which precisely measured cerebral activity over the frontal lobe. These students were tasked with watching a set of ten 2-minute-long videos.…”

Section: B Eeg Dataset Background and Descriptionmentioning

confidence: 99%

“…Each student is equipped with a wireless single-channel Neurosky MindSet EEG headset. [27], which precisely measured cerebral activity over the frontal lobe.…”

Section: Eeg Dataset Background and Descriptionmentioning

confidence: 99%

ODL-BCI: Optimal deep learning model for brain-computer interface to classify students confusion via hyperparameter tuning

Miah,

Habiba,

Kabir

2023

Preprint

View full text Add to dashboard Cite

Brain-computer interface (BCI) research has gained attention in education contexts, offering the potential to monitor and enhance student’s cognitive states and this study focuses on developing an optimal deep learning model, ODL-BCI, for real-time classification of students’ concentration levels. The model incorporates hyper-parameter tuning techniques and utilizes the publicly available “Confused student EEG brainwave data” dataset. We propose a deep learning model with hyperparameters optimized through Bayesian optimization. The model’s architecture is constructed with an input layer, several hidden layers, and an output layer. The number of nodes and activation functions in the hidden layers is determined using Bayesian optimization. The learning rate is also optimized for each layer. The proposed model is evaluated and compared with several standard machine learning classifiers, including Decision Tree, AdaBoost, Bagging, MLP, Naïve Bayes, Random Forest, SVM, and XG Boost, on an EEG confusion dataset. Experimental results demonstrate that the optimized deep learning model outperforms all other classifiers, achieving an accuracy of 74 percent. The model’s effectiveness in accurately classifying students’ concentration levels highlights its potential as a valuable tool in educational settings. This research contributes to the advancement of BCI technology, providing insights into the optimization of deep learning models for EEG-based cognitive assessment. Future work involves exploring the model’s generalizability on larger datasets and extending its applicability to other BCI applications.

show abstract

Long-Term Coronary Artery Disease Risk Prediction with Machine Learning Models

Cited by 33 publications

References 71 publications

Enhancing Heart Disease Prediction Accuracy through Machine Learning Techniques and Optimization

Enhancing Heart Disease Prediction Accuracy through Machine Learning Techniques and Optimization

Machine Learning based Prediction and Diagnosis of Heart Disease using multiple models

ODL-BCI: Optimal deep learning model for brain-computer interface to classify students confusion via hyperparameter tuning

Contact Info

Product

Resources

About