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Improving Coronary Heart Disease Prediction Through Machine Learning and an Innovative Data Augmentation Technique

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Abstract

Coronary heart disease (CHD) is a leading cause of death globally, with over 382,000 deaths in the USA alone in 2020. The early detection of CHD is critical in reducing mortality rates. Artificial intelligence (AI) is a constantly evolving field of computer science that employs computational models to extract insights from past data and provide rapid and accurate predictions for future cases. This paper presents a novel approach that generates an augmented dataset by selectively duplicating misclassified instances during the leave-one-out cross-validation (CV) process to overfit a model. We used a paired machine learning model with an augmented dataset approach to evaluate several classifiers. The comprehensive heart disease dataset [1] served as our base dataset. Our approach achieved higher accuracy than the base dataset, with the bagged decision tree (DT) algorithm outperforming state-of-the-art models and achieving an accuracy of 97.1% in the 10-fold CV test. Further experiments using the Cleveland dataset and the same 10-fold CV test resulted in an even higher accuracy of 99.2%. Combining an augmented dataset and the bagged-DT algorithm holds great promise for early CHD prediction helping reduce CHD mortality rates. The use of AI in early CHD prediction could potentially make a difference between the life and death of the patient.

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Data Availability

The datasets are available for non-commercial use. Download from https://github.com/Abdulrakeeb/Heart-disease-dataset.

Notes

  1. Heart Disease Facts, https://www.cdc.gov/heartdisease/facts.htm.

  2. The report lists death per country for the latest available year.

  3. After the repository holding the individual datasets, the UCI (UC Irvine) machine learning repository. The two sets of numbers refer to the number of instances and the number of features.

  4. https://www.kaggle.com/johnsmith88/heart-disease-dataset.

  5. Downloaded from https://ieee-dataport.org/open-access/heart-disease-dataset-comprehensive.

  6. Available at https://scikit-learn.org/.

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Acknowledgements

The authors would like to thank the anonymous reviewers for their critical review of the paper.

Funding

The authors extend their appreciation to the Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia for funding this research work through the project no. IFKSURG-2-23. The funding sponsors were not involved in any matters related to research.

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Authors and Affiliations

  1. Department of Computer Science, Faculty of Applied Sciences, Taiz University, Taiz, Yemen

    Abdulrakeeb M. Al-Ssulami & Randh S. Alsorori

  2. Department of Computer Science, College of Computer & Information Sciences, King Saud University, Riyadh, 11543, Saudi Arabia

    Aqil M. Azmi & Hatim Aboalsamh

Authors
  1. Abdulrakeeb M. Al-Ssulami
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  2. Randh S. Alsorori
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  3. Aqil M. Azmi
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  4. Hatim Aboalsamh
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Correspondence to Aqil M. Azmi.

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Al-Ssulami, A.M., Alsorori, R.S., Azmi, A.M. et al. Improving Coronary Heart Disease Prediction Through Machine Learning and an Innovative Data Augmentation Technique. Cogn Comput 15, 1687–1702 (2023). https://doi.org/10.1007/s12559-023-10151-6

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