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Back propagation artificial neural network for diagnose of the heart disease

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Abstract

Nowadays, coronary heart disease is one of the most fatal disease globally. Many researchers and medical technicians have developed and designed various computer-aided diagnosis systems using various machine learning models such as random forest, linear regression, rough set, naive bayes, artificial neural network, support vector machine, multivariate adaptive regression splines, K-nearest neighbor and decision tree to name a few. This era of digital health domain demands early prediction of heart disease which is the crucial need to control the global mortality rate of this particular disease. Commonly used methods for heart disease detection are clinical and expert dependant which makes them costly and inaccessible to the masses. In this paper, an efficient-cum-automated coronary heart disease diagnosis model is being proposed using multi-layered artificial neural network with back propagation algorithm. The proposed model compares the variation caused by different number of neurons used in the hidden layers for different transfer functions. The model has been implemented on Kaggle and Statlog heart disease dataset with thirteen clinical parameters. The experimental results attained an accuracy of 99.92% using six hidden layers with tan-hyperbolic transfer function. The results have been substantiated through statistical parameters and k-fold cross validation.

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Author notes

  1. Baljit S. Khehra and Amarinder Singh are contributed equally to this work.

Authors and Affiliations

  1. IKG Punjab Technical University, Jalandhar, Punjab, 144103, India

    Jagmohan Kaur

  2. Computer Science and Engineering, BBSB Engineering College, Fatehgarh Sahib, Punjab, 140407, India

    Baljit S. Khehra

  3. Department of Applied Sciences, BBSB Engineering College, Fatehgarh Sahib, Punjab, 140407, India

    Amarinder Singh

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  1. Jagmohan Kaur
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Correspondence to Jagmohan Kaur.

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Kaur, J., Khehra, B.S. & Singh, A. Back propagation artificial neural network for diagnose of the heart disease. J Reliable Intell Environ 9, 57–85 (2023). https://doi.org/10.1007/s40860-022-00192-3

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