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Novel Method of Characterization of Heart Disease Prediction Using Sequential Feature Selection-Based Ensemble Technique

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Abstract

The exact forecast of heart disease is necessary to proficiently treating cardiovascular patients before a heart failure happens. Assuming we talk about artificial intelligence (AI) techniques, can be accomplished utilizing an ideal AI model with rich medical services information on heart diseases. To begin with, the feature extraction technique, gradient boosting-based sequential feature selection (GBSFS) is applied to separate the significant number of features from coronary illness dataset to create important medical services information. Using machine learning algorithms like Decision tree (DT), Random forest (RF), Multilayer perceptron (MLP), Support vector machine (SVM), Extra tree (ET), Gradient boosting (GBC), Linear regression (LR), K-nearest neighbor (KNN), and stacking, a comparison model is created between dataset that include both all features and a significant number of features. With stacking, the proposed framework achieves test accuracy of 98.78 percent, which is higher than the existing frameworks and most notable in the marking model with 11 features. This outcome shows that our framework is more powerful for the expectation of coronary illness, in contrast with other cutting edge strategies.

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Data availability

The data used in this article can be accessed via the link https://www.kaggle.com/datasets/johnsmith88/heart-disease-dataset?select=heart.csv at Kaggle archive.

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Authors and Affiliations

  1. Department of Computer Applications, MHPGC, VBS Purvanchal University, Jaunpur, India

    Vikas Chaurasia

  2. Research Enthusiastic, Jaunpur, India

    Aparna Chaurasia

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  1. Vikas Chaurasia
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  2. Aparna Chaurasia
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Correspondence to Vikas Chaurasia.

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Chaurasia, V., Chaurasia, A. Novel Method of Characterization of Heart Disease Prediction Using Sequential Feature Selection-Based Ensemble Technique. Biomedical Materials & Devices 1, 932–941 (2023). https://doi.org/10.1007/s44174-022-00060-x

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