Enhancing Heart Disease Prediction Accuracy through Machine Learning Techniques and Optimization

Abstract

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.

Explainable AI: Machine Learning Interpretation in Blackcurrant Powders

“…At the initialization of each model, hyperparameters such as max_depth (3 or 5), criterion ('gini'), splitter ('best'), n_estimators (100 or 1000), and learning_rate (0.95) are determined, which are used to evaluate their performance. The performance of the models was determined using quality indicators such as accuracy, precision, recall, and F1-score, which were expressed according to the following formulas [41,[51][52][53]:…”

A Review of Machine Learning’s Role in Cardiovascular Disease Prediction: Recent Advances and Future Challenges

“…In healthcare systems, features are considered as input variables that describe the characteristics of patients [58]. Each individual (patient) in the dataset is represented by a set of feature values [29,59]. In particular, healthcare datasets may contain irrelevant features that may introduce noise into the model, hence leading to decreased prediction accuracy [60].…”

“…These metrics are computed using the confusion matrix. The common evaluation metrics used to assess the performance of machine learning models in cardiovascular disease prediction, including accuracy, recall (sensitivity), specificity, precision, F1-score, Matthews correlation coefficient (MCC), the area under the curve (AUC) and receiver operating characteristic (ROC) curve [29,62,[176][177][178][179][180]. In cardiovascular disease prediction, evaluation criteria are crucial [17].…”

“…Machine learning is a subset of artificial intelligence (AI) that uses algorithms to allow computer agents to perceive, acquire knowledge, identify patterns, and make intelligent decisions by analyzing collected data [26][27][28]. With its ability to evaluate enormous amounts of patient data, machine learning has emerged as a key player for achieving accurate and trustworthy cardiovascular disease prediction [29]. The predictive power of machine learning techniques has emerged as a promising path for revolutionizing the management of cardiovascular disease [17,30,31].…”

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

“…Similarly, Mohapatra et al ( 6 ) utilized stacking classifiers for their predictive model, achieving 92% accuracy. Chandrasekhar and Peddakrishna ( 7 ) further enhanced prediction using a soft voting ensemble classifier, marking an accuracy of 95% on the IEEE Dataport dataset. Optimization techniques have also been at the forefront of these advancements.…”