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

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

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, part...

Full description

Saved in:
Bibliographic Details
Published in:Journal of medical systems Vol. 48; no. 1; p. 53
Main Authors: Sayed, Mohammad Saiduzzaman, Rony, Mohammad Abu Tareq, Islam, Mohammad Shariful, Raza, Ali, Tabassum, Sawsan, Daoud, Mohammad Sh, Migdady, Hazem, Abualigah, Laith
Format: Journal Article
Language:English
Published: New York Springer US 22-05-2024
Springer Nature B.V
Subjects:
Autoregressive models
Cardiac muscle
Data collection
Deep Learning
Disaster management
Early warning systems
Emergency communications systems
Emergency preparedness
Forecasting
Forecasting - methods
Health Informatics
Health Sciences
Heart attacks
Humans
Incidence
Medicine
Medicine & Public Health
Metropolitan areas
Myocardial infarction
Myocardial Infarction - diagnosis
Myocardial Infarction - epidemiology
Original Paper
Public awareness
Public health
Quality control
Resource allocation
Seasonal variations
Seasons
Statistics for Life Sciences
Time series
Warning systems
Myocardial infarction
Deep learning
Time series forecasting
LSTM
Heart attack
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1573-689X
0148-5598
1573-689X
DOI:10.1007/s10916-024-02076-w