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W3136365253 abstract "AbstractRecent studies show that heart attack is one of the severe problems in today’s world. Prediction is one of the crucial challenges in the medical field. In the heart, there are two main blood vessels for the supply of blood through coronary arteries. If the arteries get completely blocked, then it leads to a heart attack. The healthcare field has lots of data related to different diseases, so machine learning techniques are useful to find results effectively for predicting heart diseases. In this paper, data was preprocessed in order to remove the noisy data, filling the missing values using measures of central tendencies. Later, the refined dataset was classified using classifiers apart from prediction. The numbers of attributes were reduced using dimensionality reduction techniques namely Linear Transformation Techniques (LTT) like Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). The performances of the classifiers were analyzed based on various accuracy-related metrics. The designed classifier model is able to predict the occurrence of a heart attack. The Support Vector Machine (SVM) classifier was applied along with the three kernels namely Linear (linear), Radial Basis Function (RBF), and Polynomial (poly). Another technique namely Decision Tree (DT) was also applied on the Cleveland dataset, and the results were compared in detail and effective conclusions were drawn from the results.KeywordsHeart attackSupport vector machineLinear transformation techniques (LTT)Principal component analysis (PCA)Linear discriminant analysis (LDA)" @default.
W3136365253 created "2021-03-29" @default.
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W3136365253 date "2021-01-01" @default.
W3136365253 modified "2023-09-25" @default.
W3136365253 title "Heart Attack Classification Using SVM with LDA and PCA Linear Transformation Techniques" @default.
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W3136365253 doi "https://doi.org/10.1007/978-981-33-4046-6_10" @default.
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