Abstract
A great diversity comes in the field of medical sciences because of computing capabilities and improvements in techniques, especially in the identification of human heart diseases. Nowadays, it is one of the world’s most dangerous human heart diseases and has very serious effects the human life. Accurate and timely identification of human heart disease can be very helpful in preventing heart failure in its early stages and will improve the patient’s survival. Manual approaches for the identification of heart disease are biased and prone to interexaminer variability. In this regard, machine learning algorithms are efficient and reliable sources to detect and categorize persons suffering from heart disease and those who are healthy. According to the recommended study, we identified and predicted human heart disease using a variety of machine learning algorithms and used the heart disease dataset to evaluate its performance using different metrics for evaluation, such as sensitivity, specificity, F-measure, and classification accuracy. For this purpose, we used nine classifiers of machine learning to the final dataset before and after the hyperparameter tuning of the machine learning classifiers, such as AB, LR, ET, MNB, CART, SVM, LDA, RF, and XGB. Furthermore, we check their accuracy on the standard heart disease dataset by performing certain preprocessing, standardization of dataset, and hyperparameter tuning. Additionally, to train and validate the machine learning algorithms, we deployed the standard K-fold cross-validation technique. Finally, the experimental result indicated that the accuracy of the prediction classifiers with hyperparameter tuning improved and achieved notable results with data standardization and the hyperparameter tuning of the machine learning classifiers.
Highlights
As per the World Health Organization (WHO) report, 17.9 million deaths occurred from cardiovascular diseases (CVDs) in 2019, representing 32% of all global deaths [1] and having an annual mortality rate of greater than 17.7 million [2]
We proposed a machine learning classifier that includes random forest (RF), XGBoost (XGB), decision trees (CART), support vector machine (SVM), multinomial Naıve Bayes (MNB), logistic regression (LR), linear discriminant analysis (LDA), AdaBoost classifier (AB), and extra trees classifier (ET) for heart disease prediction. e standardization and hyperparameters are performed using the GridSearch CV method to select the best value for the hyperparameters for the best machine learning classifier
To evaluate the system’s performance, recall, accuracy, precision, and F-measure are employed, and model training and testing are carried out using the UCI repository of machine learning Cleveland dataset, which consists of the records of 303 instances and has 76 attributes. rough preprocessing, missing values were removed from the data, which consisted of six records, and the 14 most relevant attributes of the heart disease were used. e results generated during the experiment showed that MLN-NN obtained a higher accuracy of 93.39%, with a running time of 3.86 seconds
Summary
As per the World Health Organization (WHO) report, 17.9 million deaths occurred from cardiovascular diseases (CVDs) in 2019, representing 32% of all global deaths [1] and having an annual mortality rate of greater than 17.7 million [2]. The predictive models of machine learning require appropriate data. When a refined/ standardized dataset is used for training and testing, the accuracy of machine learning classifiers can be improved. Numerous machine learning-based methods have been proposed for predicting the risk of CSD. Most of these methods exploit the use of publicly available datasets for the purpose of model training and evaluation. For enhanced cardiac disease prediction, researchers have developed a variety of machine learning models, such as SVM, KNN, FR, DT, LR, NB, and so on. The proposed machine learning classifiers’ accuracy has been compared to existing state-of-the-art methods in the literature, such as SVM, LR [19], and RF [20].
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