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

IF 3.5 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Journal of Medical Systems Pub Date : 2024-05-22 DOI:10.1007/s10916-024-02076-w
Mohammad Saiduzzaman Sayed, Mohammad Abu Tareq Rony, Mohammad Shariful Islam, Ali Raza, Sawsan Tabassum, Mohammad Sh Daoud, Hazem Migdady, Laith Abualigah
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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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利用时间序列患者数据预测心肌梗死发生率的新型深度学习方法。
心肌梗塞(MI)通常被称为心脏病发作,是由于部分心肌供血突然受阻,导致受影响的组织因缺氧而恶化或死亡。心肌梗死是全球关注的重大公共卫生问题,对吉大港大都市区的居民影响尤为严重。由于心肌缺血的出现给居民带来了巨大的痛苦,因此预防和治疗都面临着挑战。早期预警系统对于及时处理流行病至关重要,特别是考虑到老年人群的疾病负担不断加重,以及评估当前和未来需求的复杂性。本研究的主要目的是利用深度学习模型及早预测心肌梗死的发病率,预测患者心脏病发作的流行率。我们的方法涉及从吉大港大都市区 2020 年 1 月 1 日至 2021 年 12 月 31 日期间每日心脏病发作发病率时间序列患者数据中收集的新型数据集。最初,我们应用了各种先进的模型,包括自回归综合移动平均(ARIMA)、误差-趋势-季节(ETS)、三角季节性、箱-考克斯变换、ARMA 误差、趋势和季节(TBATS)以及长短时间记忆(LSTM)。为了提高预测的准确性,我们提出了一种新的心肌序列分类(MSC)-LSTM 方法,利用从吉大港大都市区收集的新数据预测心脏病患者的发病率。综合结果比较显示,新型 MSC-LSTM 模型的性能优于其他应用模型,平均百分比误差 (MPE) 最小值为 1.6477。这项研究有助于预测未来心脏病发作的可能过程,从而为制定未来预防措施的周密计划提供便利。对心肌梗死发生率的预测有助于有效的资源分配、能力规划、政策制定、预算编制、公众意识、研究鉴定、质量改进和备灾。
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来源期刊
Journal of Medical Systems
Journal of Medical Systems 医学-卫生保健
CiteScore
11.60
自引率
1.90%
发文量
83
审稿时长
4.8 months
期刊介绍: Journal of Medical Systems provides a forum for the presentation and discussion of the increasingly extensive applications of new systems techniques and methods in hospital clinic and physician''s office administration; pathology radiology and pharmaceutical delivery systems; medical records storage and retrieval; and ancillary patient-support systems. The journal publishes informative articles essays and studies across the entire scale of medical systems from large hospital programs to novel small-scale medical services. Education is an integral part of this amalgamation of sciences and selected articles are published in this area. Since existing medical systems are constantly being modified to fit particular circumstances and to solve specific problems the journal includes a special section devoted to status reports on current installations.
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