Abstract
Predicting heart disease needs more perfection, precision, and correctness because a little fault may cause a big danger for a patient. In the field of machine learning, there are many classification algorithms for predicting heart disease. This paper presents the probability of heart disease prediction by some machine learning classifiers which are processed by feature engineering techniques on datasets. Feature engineering is used for building features by the process of using domain knowledge of data. Here a comparison has been shown before and after feature engineering of those supervised learning algorithms and identified the best algorithm for the best accuracy. The performance of each algorithm is determined and a comparison is made for each algorithm based on the precision of the calculation and the evaluation time. The proposed method has used the Cleveland dataset and another dataset consists of four datasets (Switzerland, Hungary, Cleveland, and Long Beach) downloaded from the Kaggle repository. Here the better accuracy has been gained from Ridge Classifier 86.89% for the Cleveland database. Another dataset has given 100% accuracy for the Gradient Boosting classifier, Bagging Classifier, and Gaussian Process classifier. This research will help to predict heart disease at an early stage which will reduce the death rate of heart disease.
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Alam, N., Alam, S., Tasnim, F., Sharmin, S. (2023). Improved and Intelligent Heart Disease Prediction System Using Machine Learning Algorithm. In: Satu, M.S., Moni, M.A., Kaiser, M.S., Arefin, M.S. (eds) Machine Intelligence and Emerging Technologies. MIET 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 490. Springer, Cham. https://doi.org/10.1007/978-3-031-34619-4_9
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