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<p>Stroke is a leading cause of disability and mortality globally, imposing a significant impact on individuals, healthcare systems, and society. Existing diagnostic techniques for stroke prediction, including risk scores and imaging assessments, frequently lack the accuracy required for personalised treatment. Conversely, machine learning (ML) and deep learning (DL) models provide the capability for enhanced accuracy and personalised predictions by employing complex high-dimensional data sets. However, these methodologies experience challenges such as generalisability, interpretability (blackbox ML algorithm), and data quality. This systematic review, which does not strictly adhere to all PRISMA guidelines, aims to provide an extensive overview of stroke prediction methods by offering transparent methodology descriptions and employing a PICO-based search strategy to analyse studies from 1981 to 2024, focussing on stroke prediction, diagnosis, prognosis, and treatment. A total of 3064 articles were initially identified from major databases (PubMed, Medline, EMBASE), with 226 studies included following a thorough screening process. The review integrated findings from traditional clinical instruments and modern AI methodologies, highlighting data types (imaging, signal, and tabular), biomarkers, model efficacy, and clinical relevance. Machine learning and deep learning models displayed enhanced predictive accuracy and capability for personalised stroke risk classification, especially when incorporating multimodal data, including neuroimaging, electrocardiograms, and biomarkers such as plasma fibrinogen. Advanced models such as CNNs and LSTMs demonstrate superior performance compared to traditional tools in the processing of complex data; however, they are constrained by issues related to generalisability and interpretability. Classical tools continue to hold significance, particularly in low-resource environments and initial stroke evaluations. Hybrid approaches that integrate machine learning with traditional methods demonstrate potential for improved clinical utility. AI-driven methods indicate a notable development in stroke prediction; however, their incorporation into clinical practice requires the resolution of critical limitations, including model transparency, data diversity, and ethical considerations. Future research must emphasise explainable AI, multimodal data fusion, and equitable access to guarantee that machine learning-enhanced tools are accurate, interpretable, and scalable across varied patient populations. Such initiatives are essential for enhancing stroke prevention, diagnosis, and management globally.</p>

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Stroke Risk Prediction and Prevention: Traditional versus Machine Learning Approaches

  • 期刊论文

  • 发表日期：

    2025年9月21日

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  • M. Sheetal Singh,
  • Khelchandra Thongam,
  • Prakash Choudhary,
  • P. K. Bhagat

摘要

Stroke is a leading cause of disability and mortality globally, imposing a significant impact on individuals, healthcare systems, and society. Existing diagnostic techniques for stroke prediction, including risk scores and imaging assessments, frequently lack the accuracy required for personalised treatment. Conversely, machine learning (ML) and deep learning (DL) models provide the capability for enhanced accuracy and personalised predictions by employing complex high-dimensional data sets. However, these methodologies experience challenges such as generalisability, interpretability (blackbox ML algorithm), and data quality. This systematic review, which does not strictly adhere to all PRISMA guidelines, aims to provide an extensive overview of stroke prediction methods by offering transparent methodology descriptions and employing a PICO-based search strategy to analyse studies from 1981 to 2024, focussing on stroke prediction, diagnosis, prognosis, and treatment. A total of 3064 articles were initially identified from major databases (PubMed, Medline, EMBASE), with 226 studies included following a thorough screening process. The review integrated findings from traditional clinical instruments and modern AI methodologies, highlighting data types (imaging, signal, and tabular), biomarkers, model efficacy, and clinical relevance. Machine learning and deep learning models displayed enhanced predictive accuracy and capability for personalised stroke risk classification, especially when incorporating multimodal data, including neuroimaging, electrocardiograms, and biomarkers such as plasma fibrinogen. Advanced models such as CNNs and LSTMs demonstrate superior performance compared to traditional tools in the processing of complex data; however, they are constrained by issues related to generalisability and interpretability. Classical tools continue to hold significance, particularly in low-resource environments and initial stroke evaluations. Hybrid approaches that integrate machine learning with traditional methods demonstrate potential for improved clinical utility. AI-driven methods indicate a notable development in stroke prediction; however, their incorporation into clinical practice requires the resolution of critical limitations, including model transparency, data diversity, and ethical considerations. Future research must emphasise explainable AI, multimodal data fusion, and equitable access to guarantee that machine learning-enhanced tools are accurate, interpretable, and scalable across varied patient populations. Such initiatives are essential for enhancing stroke prevention, diagnosis, and management globally.