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A novel feature selection approach with integrated feature sensitivity and feature correlation for improved prediction of heart disease

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Abstract

This paper presents a random forest-feature sensitivity and feature correlation (RF-FSFC) technique for enhanced heart disease prediction. The proposed methodology is implemented using the Cleveland heart disease dataset which comprises a total of 120 heart disease patient records. Data imputation was utilized for missing values, and min–max normalization was utilized for data transformation. We attempted to construct an RF-based classifier for coronary heart disease in this paper by combining feature sensitivity and correlation analysis. The sensitivity-based feature selection process ranks features according to their value in assessing CHD risk, and the feature correlation analysis phase analyses if there are any correlations between features. The heart disease prediction accuracy of 81.16% was obtained using the proposed RF-FSFCA technique by omitting five features (sex, hemoglobin, TD, CRF, and cirrhosis). When compared to the Naïve Bayes, decision tree, regression analysis, and support vector machine models, the proposed model offered a higher accuracy of 86.141% without omitting any features. It also offered sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) scores of 87.321%, 87.364%, 91.23, and 91.02 respectively. Experiment findings demonstrated that the proposed RF-FSFC approach significantly improves prediction accuracy as compared to other approaches that do not use the integrated Feature selection method.

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  1. Department of Computer Science and Engineering, School of Computing, Sathyabama Institute of Science and Technology, Chennai, 600119, India

    G. Saranya & A. Pravin

  2. Department of Networking and Communications, School of Computing, SRM Institute of Science and Technology, Chennai, 603203, India

    G. Saranya

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Saranya, G., Pravin, A. A novel feature selection approach with integrated feature sensitivity and feature correlation for improved prediction of heart disease. J Ambient Intell Human Comput 14, 12005–12019 (2023). https://doi.org/10.1007/s12652-022-03750-y

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