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Integrative Multidimensional Machine Learning Models for Stroke Prognosis: Age-Stratified and History Engineered

Gawon Lee^1,2,3, Sunyoung Kwon¹, Seung-Ho Shin⁴

¹Division of DataScience, Hallym University, Chuncheon 24252, Republic of Korea.

Diagnostics (Basel, Switzerland)

|May 13, 2026

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This summary is machine-generated.

Machine learning models accurately predict stroke patient mortality by integrating vital signs, lab results, and medical history. Pulse rate is a key predictor, especially in younger patients, enabling personalized stroke care.

Area of Science:

Neurology
Data Science
Biostatistics

Background:

Stroke is a major cause of death and disability globally.
Accurate prognosis is crucial for effective stroke management.
Previous models often neglected patient history and age-specific risks.

Purpose of the Study:

Develop and validate machine learning models for stroke mortality prediction.
Incorporate diverse patient data including vitals, labs, demographics, and history.
Explore subgroup-specific predictive factors.

Main Methods:

Retrospective analysis of 1780 stroke patients (2018-2023).
Utilized Random Forest models with original and binarized clinical data.
Assessed model performance using AUC and variable importance (Mean Decrease Gini, SHAP).

Keywords:

machine learning multidimensional analysis prognosis stroke

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Main Results:

Highest accuracy (AUC 0.995) achieved for patients under 60 using binarized data.
Pulse rate identified as the most significant predictor across models.
Platelet count and diastolic blood pressure also important; higher pulse rate linked to increased mortality risk.

Conclusions:

Integrating clinical, demographic, and historical data improves stroke mortality prediction accuracy and interpretability.
Stratified modeling and monitoring pulse rate are vital for precision stroke care.