Efficient Prediction of Cardiovascular Disease Using Machine Learning Algorithms With Relief and LASSO Feature Selection Techniques

• Published in: IEEE access 2021, Vol.9, p.19304-19326
• Main Authors: Ghosh, Pronab, Azam, Sami, Jonkman, Mirjam, Karim, Asif, Shamrat, F. M. Javed Mehedi, Ignatious, Eva, Shultana, Shahana, Beeravolu, Abhijith Reddy, De Boer, Friso
• Format: Article
• Language: English
• Subjects: AdaBoost; Algorithms; Bagging; Boosting; Classification algorithms; Classifiers; CVD; Data collection; decision tree; Decision trees; Feature extraction; Feature selection; gradient boosting; Heart; Heart disease; Heart diseases; K-nearest neighbors; LASSO feature selection; Machine learning; Mathematical analysis; Methods; Model accuracy; Prediction algorithms; Predictive models; random forest; relief feature selection; Risk analysis; Support vector machines; Training
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3053759

Cardiovascular diseases (CVD) are among the most common serious illnesses affecting human health. CVDs may be prevented or mitigated by early diagnosis, and this may reduce mortality rates. Identifying risk factors using machine learning models is a promising approach. We would like to propose a model that incorporates different methods to achieve effective prediction of heart disease. For our proposed model to be successful, we have used efficient Data Collection, Data Pre-processing and Data Transformation methods to create accurate information for the training model. We have used a combined dataset (Cleveland, Long Beach VA, Switzerland, Hungarian and Stat log). Suitable features are selected by using the Relief, and Least Absolute Shrinkage and Selection Operator (LASSO) techniques. New hybrid classifiers like Decision Tree Bagging Method (DTBM), Random Forest Bagging Method (RFBM), K-Nearest Neighbors Bagging Method (KNNBM), AdaBoost Boosting Method (ABBM), and Gradient Boosting Boosting Method (GBBM) are developed by integrating the traditional classifiers with bagging and boosting methods, which are used in the training process. We have also instrumented some machine learning algorithms to calculate the Accuracy (ACC), Sensitivity (SEN), Error Rate, Precision (PRE) and F1 Score (F1) of our model, along with the Negative Predictive Value (NPR), False Positive Rate (FPR), and False Negative Rate (FNR). The results are shown separately to provide comparisons. Based on the result analysis, we can conclude that our proposed model produced the highest accuracy while using RFBM and Relief feature selection methods (99.05%).