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Artificial intelligence for heart disease prediction and imputation of missing data in cardiovascular datasets

Cogent engineering, 2024-12, Vol.11 (1) [Peer Reviewed Journal]

EISSN: 2331-1916 ;DOI: 10.1080/23311916.2024.2325635

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  • Title:
    Artificial intelligence for heart disease prediction and imputation of missing data in cardiovascular datasets
  • Author: Ahmed Haitham Najim ; Nejah Nasri
  • Subjects: Cardiovascular ; Chang Gung University ; extreme machine learning ; heart diseases ; Jenhui Chen ; machine learning
  • Is Part Of: Cogent engineering, 2024-12, Vol.11 (1)
  • Description: AbstractAccording to World Health Organization (WHO) data, cardiovascular diseases (CAD) continue to take the lives of more than 17.9 million people worldwide each year. Heart attacks are considered a fatal disease in this category, especially for older adults, which highlights the need to employ artificial intelligence to anticipate this disease. This research faces many challenges, starting with data quality and availability, where AI models require large and high-quality datasets for training. Elderly populations exhibit various health conditions, lifestyle factors, and genetic diversity. Creating AI models that can accurately generalize across such a diverse group can be challenging. Two datasets for CAD diseases were used for this study. Traditional machine learning (ML) techniques were used on these datasets, as well as a neural network method based on extreme learning machines (ELM), which provided varying percentages of accuracy, time, and average estimated error. The ELM algorithm outperformed all other algorithms by attaining the best accuracy, the shortest execution time, and the lowest percentage of average estimated error. Experimental results showed that the Extreme learning machine performed well with 200 hidden neurons, even with the proposed absence of parts of the dataset, with an accuracy of 97.57–99.06%.
  • Publisher: Taylor & Francis Group
  • Language: English
  • Identifier: EISSN: 2331-1916
    DOI: 10.1080/23311916.2024.2325635
  • Source: Taylor & Francis Open Access
    ProQuest Central
    DOAJ Directory of Open Access Journals
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