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A hybrid machine learning approach to identify coronary diseases using feature selection mechanism on heart disease dataset

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Abstract

Coronary illness can be treated as one of the major causes for mortality globally. On-time and Precise conclusion on the type of disease is significant for therapy and breakdown expectancy. Research scientists are working rigorously in their respective fields to reduce the death rate. Even though lot of research took place on this area still there is a scope for increasing the prediction accuracy. The fundamental point of our proposed work is to build up a hybrid methodology using genetic algorithm (GA) with (RBF) radial basis function (GA-RBF) for the detection of coronary sickness with increased accuracy using the feature selection mechanism. The proposed system performance achieved an accuracy of 85.40% using 14 attributes, and the prediction accuracy increased to 94.20% with nine characteristics where the functionality of the proposed system performed much better after attribute reduction.

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Authors and Affiliations

  1. Department of Computer Science and Multimedia, Lincoln University College, Petaling Jaya, Malaysia

    Bhanu Prakash Doppala & Midhun Chakkravarthy

  2. Department of Computer Science and Engineering, K L Deemed to be University, KLEF, Guntur, 522502, India

    Debnath Bhattacharyya

  3. School of Economics and Management, Beijing Jiaotong University, Beijing, China

    Tai-hoon Kim

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  1. Bhanu Prakash Doppala
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  2. Debnath Bhattacharyya
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  3. Midhun Chakkravarthy
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  4. Tai-hoon Kim
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Correspondence to Tai-hoon Kim.

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Doppala, B.P., Bhattacharyya, D., Chakkravarthy, M. et al. A hybrid machine learning approach to identify coronary diseases using feature selection mechanism on heart disease dataset. Distrib Parallel Databases 41, 1–20 (2023). https://doi.org/10.1007/s10619-021-07329-y

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