An extensive experimental analysis for heart disease prediction using artificial intelligence techniques
D Rohan1, G Pradeep Reddy2, Y V Pavan Kumar3
1School of Computer Science and Engineering, VIT-AP University, Amaravati, 522241, Andhra Pradesh, India.
Insights
This study explored various artificial intelligence models for heart disease prediction. XGBoost demonstrated superior performance, achieving high accuracy and other key metrics for early detection.
Area of Science:
- Cardiology
- Artificial Intelligence
- Machine Learning
Background:
- Heart disease is a leading global cause of mortality.
- Early and accurate detection of heart disease is critical for effective treatment and prevention.
- Artificial intelligence (AI) offers promising solutions for healthcare, particularly in disease prediction.
Purpose of the Study:
- To investigate and compare the effectiveness of various machine learning models for heart disease prediction.
- To identify the optimal model for accurate and reliable heart disease detection.
Main Methods:
- The study evaluated 11 feature selection techniques and 21 distinct classification algorithms.
- Feature selection methods included Information Gain, Chi-Square Test, FDA, PCA, and others.
- Classifiers encompassed Logistic Regression, SVM, Random Forest, XGBoost, various neural networks (CNN, RNN, LSTM), and hybrid models.
Main Results:
- XGBoost significantly outperformed all other evaluated models.
- XGBoost achieved an accuracy of 0.97, precision of 0.97, sensitivity of 0.98, specificity of 0.98, F1 score of 0.98, and AUC of 0.98.
- These results indicate XGBoost's high efficacy in heart disease prediction.
Conclusions:
- The research highlights the potential of AI, specifically XGBoost, in improving heart disease prediction accuracy.
- The findings suggest that XGBoost can be a valuable tool for early detection and personalized healthcare recommendations.
- Further experimentation with diverse models is crucial for advancing AI in cardiovascular health.
Abstract:
The heart is an important organ that plays a crucial role in maintaining life. Unfortunately, heart disease is one of the major causes of mortality globally. Early and accurate detection can significantly improve the situation by enabling preventive measures and personalized healthcare recommendations. Artificial intelligence is emerging as a powerful tool for healthcare applications, particularly in predicting heart diseases. Researchers are actively working on this, but challenges remain in achieving accurate heart disease prediction. Therefore, experimenting with various models to identify the most effective one for heart disease prediction is crucial. In this view, this paper addresses this need by conducting an extensive investigation of various models. The proposed research considered 11 feature selection techniques and 21 classifiers for the experiment. The feature selection techniques considered for the research are Information Gain, Chi-Square Test, Fisher Discriminant Analysis (FDA), Variance Threshold, Mean Absolute Difference (MAD), Dispersion Ratio, Relief, LASSO, Random Forest Importance, Linear Discriminant Analysis (LDA), and Principal Component Analysis (PCA). The classifiers considered for the research are Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Gaussian Naïve Bayes (GNB), XGBoost, AdaBoost, Stochastic Gradient Descent (SGD), Gradient Boosting Classifier, Extra Tree Classifier, CatBoost, LightGBM, Multilayer Perceptron (MLP), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Bidirectional LSTM (BiLSTM), Bidirectional GRU (BiGRU), Convolutional Neural Network (CNN), and Hybrid Model (CNN, RNN, LSTM, GRU, BiLSTM, BiGRU). Among all the extensive experiments, XGBoost outperformed all others, achieving an accuracy of 0.97, precision of 0.97, sensitivity of 0.98, specificity of 0.98, F1 score of 0.98, and AUC of 0.98.
