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The correct diagnosis of heart disease can save lives, while the incorrect diagnosis can be lethal. The UCI machine learning heart disease dataset compares the results and analyses of various machine learning approaches, including deep learning. We used a dataset with 13 primary characteristics to carry out the research. Support vector machine and logistic regression algorithms are used to process the datasets, and the latter displays the highest accuracy in predicting coronary disease. Python programming is used to process the datasets. Multiple research initiatives have used machine learning to speed up the healthcare sector. We also used conventional machine learning approaches in our investigation to uncover the links between the numerous features available in the dataset and then used them effectively in anticipation of heart infection risks. Using the accuracy and confusion matrix has resulted in some favorable outcomes. To get the best results, the dataset contains certain unnecessary features that are dealt with using isolation logistic regression and Support Vector Machine (SVM) classification.


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A Clinical Data Analysis Based Diagnostic Systems for Heart Disease Prediction Using Ensemble Method

Show Author's information Ankit Kumar1Kamred Udham Singh2( )Manish Kumar3
Department of Computer Engineering and Applications, GLA University, Mathura 281406, India
School of Computing, Graphic Era Hill University, Dehradun 248002, India
Department of Electronics and Communication Engineering, GLA University, Mathura 281406, India

Abstract

The correct diagnosis of heart disease can save lives, while the incorrect diagnosis can be lethal. The UCI machine learning heart disease dataset compares the results and analyses of various machine learning approaches, including deep learning. We used a dataset with 13 primary characteristics to carry out the research. Support vector machine and logistic regression algorithms are used to process the datasets, and the latter displays the highest accuracy in predicting coronary disease. Python programming is used to process the datasets. Multiple research initiatives have used machine learning to speed up the healthcare sector. We also used conventional machine learning approaches in our investigation to uncover the links between the numerous features available in the dataset and then used them effectively in anticipation of heart infection risks. Using the accuracy and confusion matrix has resulted in some favorable outcomes. To get the best results, the dataset contains certain unnecessary features that are dealt with using isolation logistic regression and Support Vector Machine (SVM) classification.

Keywords: artificial intelligence, support vector machine, logistic regression, cleveland dataset, supervised algorithm, human sensing

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Received: 29 July 2022
Revised: 10 December 2022
Accepted: 29 December 2022
Published: 29 August 2023
Issue date: December 2023

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