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A Novel Deep Learning Approach for Forecasting Myocardial Infarction Occurrences with Time Series Patient Data

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Abstract

Myocardial Infarction (MI) commonly referred to as a heart attack, results from the abrupt obstruction of blood supply to a section of the heart muscle, leading to the deterioration or death of the affected tissue due to a lack of oxygen. MI, poses a significant public health concern worldwide, particularly affecting the citizens of the Chittagong Metropolitan Area. The challenges lie in both prevention and treatment, as the emergence of MI has inflicted considerable suffering among residents. Early warning systems are crucial for managing epidemics promptly, especially given the escalating disease burden in older populations and the complexities of assessing present and future demands. The primary objective of this study is to forecast MI incidence early using a deep learning model, predicting the prevalence of heart attacks in patients. Our approach involves a novel dataset collected from daily heart attack incidence Time Series Patient Data spanning January 1, 2020, to December 31, 2021, in the Chittagong Metropolitan Area. Initially, we applied various advanced models, including Autoregressive Integrated Moving Average (ARIMA), Error-Trend-Seasonal (ETS), Trigonometric seasonality, Box-Cox transformation, ARMA errors, Trend and Seasonal (TBATS), and Long Short Time Memory (LSTM). To enhance prediction accuracy, we propose a novel Myocardial Sequence Classification (MSC)-LSTM method tailored to forecast heart attack occurrences in patients using the newly collected data from the Chittagong Metropolitan Area. Comprehensive results comparisons reveal that the novel MSC-LSTM model outperforms other applied models in terms of performance, achieving a minimum Mean Percentage Error (MPE) score of 1.6477. This research aids in predicting the likely future course of heart attack occurrences, facilitating the development of thorough plans for future preventive measures. The forecasting of MI occurrences contributes to effective resource allocation, capacity planning, policy creation, budgeting, public awareness, research identification, quality improvement, and disaster preparedness.

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Funding

Not Applicable.

Author information

Authors and Affiliations

  1. Department of Statistics, Jahangirnagar University, Dhaka, Bangladesh

    Mohammad Saiduzzaman Sayed

  2. Department of Statistics, Noakhali Science & Technology University, Noakhali, 3814, Bangladesh

    Mohammad Abu Tareq Rony & Sawsan Tabassum

  3. Department of Computer Science and Telecommunication Engineering, Noakhali Science & Technology University, Noakhali, 3814, Bangladesh

    Mohammad Shariful Islam

  4. Department of Software Engineering, University Of Lahore, Lahore, 54000 , Pakistan

    Ali Raza

  5. College of Engineering, Al Ain University, Abu Dhabi, 112612, UAE

    Mohammad Sh. Daoud

  6. CSMIS Department, Oman College of Management and Technology, Barka, 320, Oman

    Hazem Migdady

  7. Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, 19328, Jordan

    Laith Abualigah

  8. Computer Science Department, Al al-Bayt University, Mafraq, 25113, Jordan

    Laith Abualigah

  9. MEU Research Unit, Middle East University, Amman, 11831, Jordan

    Laith Abualigah

  10. Applied science research center, Applied science private university, Amman, 11931, Jordan

    Laith Abualigah

  11. Artificial Intelligence and Sensing Technologies (AIST) Research Center, University of Tabuk, Tabuk, 71491, Saudi Arabia

    Laith Abualigah

Authors
  1. Mohammad Saiduzzaman Sayed
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  2. Mohammad Abu Tareq Rony
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  8. Laith Abualigah
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Contributions

Mohammad Saiduzzaman Sayed: Software, Resources, Writing - original draft, Supervision, Methodology, Conceptualization, Formal analysis, Review & editing. Mohammad Abu Tareq Rony: Supervision, Methodology, Conceptualization, Writing - original draft. Mohammad Shariful Islam: Formal analysis, Writing - review & editing. Sawsan Tabassum: Formal analysis, Writing - review & editing. Ali Raza: Formal analysis, Writing - review & editing. Mohammad Sh. Daoud: Formal analysis, Writing - review & editing. Hazem Migdady: Formal analysis, Writing - review & editing. Laith Abualigah: Formal analysis, Writing - review & editing. All authors read and approved the final paper

Corresponding authors

Correspondence to Ali Raza or Laith Abualigah.

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Sayed, M.S., Rony, M.A.T., Islam, M.S. et al. A Novel Deep Learning Approach for Forecasting Myocardial Infarction Occurrences with Time Series Patient Data. J Med Syst 48, 53 (2024). https://doi.org/10.1007/s10916-024-02076-w

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