Heart Attack Classification Using SVM with LDA and PCA Linear Transformation Techniques

Kumar, Suneet; Rajinikanth, T. V.; Raju, S. Viswanadha

doi:10.1007/978-981-33-4046-6_10

Cited by 1 publication

(1 citation statement)

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“…The samples are divided into two categories by constructing this dividing hyperplane [42]. The sample points closest to the hyperplane are called support vectors, and the distance between these points and the segmentation plane is called the interval [43]. By maximizing the distance interval between the support vector and the segmentation plane, the algorithm performance is optimized, thereby enhancing the reliability of the classifier's prediction [44].…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Development of heart attack prediction model based on ensemble learning

Hasan

Saleh

2021

EEJET

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With the advent of the data age, the continuous improvement and widespread application of medical information systems have led to an exponential growth of biomedical data, such as medical imaging, electronic medical records, biometric tags, and clinical records that have potential and essential research value. However, medical research based on statistical methods is limited by the class and size of the research community, so it cannot effectively perform data mining for large-scale medical information. At the same time, supervised machine learning techniques can effectively solve this problem. Heart attack is one of the most common diseases and one of the leading causes of death, so finding a system that can accurately and reliably predict early diagnosis is an essential and influential step in treating such diseases. Researchers have used various data mining and machine learning techniques to analyze medical data, helping professionals predict heart disease. This paper presents various features related to heart disease, and the model is based on ensemble learning. The proposed system involves preprocessing data, selecting attributes, and then using logistic regression algorithms as meta-classifiers to build the ensemble learning model. Furthermore, using machine learning algorithms (Support Vector Machines, Decision Tree, Random Forest, Extreme Gradient Boosting) for prediction on the Framingham Heart Study dataset and compared with the proposed methodology. The results show that the feasibility and effectiveness of the proposed prediction method based on group learning provide accuracy for medical recommendations and better accuracy than the single traditional machine learning algorithm.

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Section: Support Vector Machine (Svm)mentioning

confidence: 99%