An efficient framework for heart disease classification using feature extraction and feature selection technique in data mining

Kavitha, R.; Kannan, E.

doi:10.1109/icetets.2016.7603000

Cited by 77 publications

(34 citation statements)

References 4 publications

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“…Kavitha and Kannan (2016) created a framework for heart-disease classification that included feature extraction using PCA. 18 The authors state the benefits of reducing the data dimensionality as increasing the prediction accuracy of the classifier and reducing the computational cost of the prediction. This can be achieved either by feature extraction methods, which create a new set of features that are somehow derived from the original features, or by feature selection, which takes a subset of the most relevant features from the dataset.…”

Section: Literature Reviewmentioning

confidence: 99%

Exploring feature selection and classification methods for predicting heart disease

et al. 2020

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Machine learning has been used successfully to improve the accuracy of computer-aided diagnosis systems. This paper experimentally assesses the performance of models derived by machine learning techniques by using relevant features chosen by various feature-selection methods. Four commonly used heart disease datasets have been evaluated using principal component analysis, Chi squared testing, ReliefF and symmetrical uncertainty to create distinctive feature sets. Then, a variety of classification algorithms have been used to create models that are then compared to seek the optimal features combinations, to improve the correct prediction of heart conditions. We found the benefits of using feature selection vary depending on the machine learning technique used for the heart datasets we consider. However, the best model we created used a combination of Chi-squared feature selection with the BayesNet algorithm and achieved an accuracy of 85.00% on the considered datasets.

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Section: Literature Reviewmentioning

confidence: 99%

Exploring feature selection and classification methods for predicting heart disease

et al. 2020

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“…In this, a new set of features is derived from the original feature set.Feature extraction involves a transformation of the features. This transformation is often not reversible asfew, or maybe many, useful information is lost in the process.In [3]and [4]Principal Component Analysis (PCA)is used for feature extraction. Principal Component Analysis is a popularly used linear transformation algorithm.…”

Section: A Feature Extractionmentioning

confidence: 99%

Heart disease prediction using machine learning techniques : a survey

Ramalingam¹,

Dandapath²,

Raja³

2018

IJET

246

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Heart related diseases or Cardiovascular Diseases (CVDs) are the main reason for a huge number of death in the world over the last few decades and has emerged as the most life-threatening disease, not only in India but in the whole world. So, there is a need of reliable, accurate and feasible system to diagnose such diseases in time for proper treatment. Machine Learning algorithms and techniques have been applied to various medical datasets to automate the analysis of large and complex data. Many researchers, in recent times, have been using several machine learning techniques to help the health care industry and the professionals in the diagnosis of heart related diseases. This paper presents a survey of various models based on such algorithms and techniques andanalyze their performance. Models based on supervised learning algorithms such as Support Vector Machines (SVM), K-Nearest Neighbour (KNN), NaïveBayes, Decision Trees (DT), Random Forest (RF) and ensemble models are found very popular among the researchers.

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“…It is online, easy to understand and expandable. To develop the multi-parametric segment with straight and nonlinear characteristics of HRV (Heart Rate Variability) a novel strategy was proposed by Heon Gyu Lee et al [21]. To finish this, they have used a couple of classifiers e.g.…”

Section: Z = ∑ Xiwimentioning

confidence: 99%

Prediction of Heart Disease using Artificial Neural Network

Awan

Riaz

Khan

2018

VFAST trans. softw. eng.

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Heart disease is increasing rapidly due to number of reasons. If we predict cardiac arrest (dangerous conditions of heart) in the early stages, it will be very helpful to cured this disease. Although doctors and health centres collect data daily, but mostly are not using machine learning and pattern matching techniques to extract the knowledge that can be very useful in prediction. Bioinformatics is the real world application of machine learning to extract patterns from the datasets using several data mining techniques. In this research paper, data and attributes are taken from the UCI repository. Attribute extraction is very effective in mining information for the prediction. By utilizing this, various patterns can be derived to predict the heart disease earlier. In this paper, we enlighten the number of techniques in Artificial Neural Network (ANN). The accuracy is calculated and visualized such as ANN gives 94.7% but with Principle Component Analysis (PCA) accuracy rate improve to 97.7%.

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An efficient framework for heart disease classification using feature extraction and feature selection technique in data mining

Cited by 77 publications

References 4 publications

Exploring feature selection and classification methods for predicting heart disease

Exploring feature selection and classification methods for predicting heart disease

Heart disease prediction using machine learning techniques : a survey

Prediction of Heart Disease using Artificial Neural Network

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