Abstract
Heart disease is one of the significant diseases which causes a huge number of deaths all over the world. Even medical specialists are facing difficulties for the proper diagnosis of heart disease which raises a need for a new classification scheme. But it becomes a crucial task for healthcare providers due to the rapid increase of medical data size every day. To resolve this, several machine learning algorithms are discussed in this paper, and these algorithms’ performance is measured by using different metrics like accuracy, precision, recall, and F1-score. But these algorithms are not acceptable for accurate prediction and diagnosis. To further improve the accuracy of classifiers, different ensemble methods were used because for any machine learning algorithm, accuracy is the main criteria to measure the performance. In this new methodology, the feature importance method is used as a pre-processing technique to get a minimum number of attributes rather than using all attributes in the dataset which has impact on the accuracy of classifiers. After that pre-processed data is trained by using various classifiers like linear regression, SVM, naïve Bayes, and decision tree, and then finally, three ensemble methods like bagging, AdaBoosting, and Gradient boosting are used to boost the performance of the classifiers. From the observations, the bagging ensemble algorithm elevated the highest accuracy, 80.21%, than the accuracy of other classifiers.
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Jalligampala, D.L.S., Lalitha, R.V.S., Anil Kumar, M., Akhila, N., Challapalli, S., Lakshmi, P.N.S. (2022). Boosting Accuracy of Machine Learning Classifiers for Heart Disease Forecasting. In: Satapathy, S.C., Peer, P., Tang, J., Bhateja, V., Ghosh, A. (eds) Intelligent Data Engineering and Analytics. Smart Innovation, Systems and Technologies, vol 266. Springer, Singapore. https://doi.org/10.1007/978-981-16-6624-7_12
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