Machine learning-based prognostication of mortality in stroke patients
Objectives: Predicting stroke mortality is crucial for personalized care. This study aims to design and evaluate a machine learning model to predict one-year mortality after a stroke. Materials and methods: Data from the National Multiethnic Stroke Registry was utilized. Eight machine learning (ML)...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-04-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024049004 |
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| author | Ahmad A. Abujaber Ibrahem Albalkhi Yahia Imam Abdulqadir Nashwan Naveed Akhtar Ibraheem M. Alkhawaldeh |
| author_facet | Ahmad A. Abujaber Ibrahem Albalkhi Yahia Imam Abdulqadir Nashwan Naveed Akhtar Ibraheem M. Alkhawaldeh |
| author_sort | Ahmad A. Abujaber |
| collection | DOAJ |
| description | Objectives: Predicting stroke mortality is crucial for personalized care. This study aims to design and evaluate a machine learning model to predict one-year mortality after a stroke. Materials and methods: Data from the National Multiethnic Stroke Registry was utilized. Eight machine learning (ML) models were trained and evaluated using various metrics. SHapley Additive exPlanations (SHAP) analysis was used to identify the influential predictors. Results: The final analysis included 9840 patients diagnosed with stroke were included in the study. The XGBoost algorithm exhibited optimal performance with high accuracy (94.5%) and AUC (87.3%). Core predictors encompassed National Institutes of Health Stroke Scale (NIHSS) at admission, age, hospital length of stay, mode of arrival, heart rate, and blood pressure. Increased NIHSS, age, and longer stay correlated with higher mortality. Ambulance arrival and lower diastolic blood pressure and lower body mass index predicted poorer outcomes. Conclusions: This model's predictive capacity emphasizes the significance of NIHSS, age, hospital stay, arrival mode, heart rate, blood pressure, and BMI in stroke mortality prediction. Specific findings suggest avenues for data quality enhancement, registry expansion, and real-world validation. The study underscores machine learning's potential for early mortality prediction, improving risk assessment, and personalized care. The potential transformation of care delivery through robust ML predictive tools for Stroke outcomes could revolutionize patient care, allowing for personalized plans and improved preventive strategies for stroke patients. However, it is imperative to conduct prospective validation to evaluate its practical clinical effectiveness and ensure its successful adoption across various healthcare environments. |
| first_indexed | 2024-04-24T12:51:23Z |
| format | Article |
| id | doaj.art-ab4ac86a335847cca2f3960ddd61f669 |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-04-24T12:51:23Z |
| publishDate | 2024-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-ab4ac86a335847cca2f3960ddd61f6692024-04-06T04:40:09ZengElsevierHeliyon2405-84402024-04-01107e28869Machine learning-based prognostication of mortality in stroke patientsAhmad A. Abujaber0Ibrahem Albalkhi1Yahia Imam2Abdulqadir Nashwan3Naveed Akhtar4Ibraheem M. Alkhawaldeh5Nursing Department, Hamad Medical Corporation, Doha, QatarCollege of Medicine, Alfaisal University, Riyadh, Saudi Arabia; Department of Neuroradiology, Great Ormond Street Hospital NHS Foundation Trust, Great Ormond St, London WC1N 3JH, United KingdomNeurology Section, Neuroscience Institute, Hamad Medical Corporation, Doha, QatarNursing Department, Hamad Medical Corporation, Doha, Qatar; Corresponding author. Nursing Department, Hamad Medical Corporation, P.O. Box 3050, Doha, Qatar.Neurology Section, Neuroscience Institute, Hamad Medical Corporation, Doha, QatarFaculty of Medicine, Mutah University, Al-Karak, JordanObjectives: Predicting stroke mortality is crucial for personalized care. This study aims to design and evaluate a machine learning model to predict one-year mortality after a stroke. Materials and methods: Data from the National Multiethnic Stroke Registry was utilized. Eight machine learning (ML) models were trained and evaluated using various metrics. SHapley Additive exPlanations (SHAP) analysis was used to identify the influential predictors. Results: The final analysis included 9840 patients diagnosed with stroke were included in the study. The XGBoost algorithm exhibited optimal performance with high accuracy (94.5%) and AUC (87.3%). Core predictors encompassed National Institutes of Health Stroke Scale (NIHSS) at admission, age, hospital length of stay, mode of arrival, heart rate, and blood pressure. Increased NIHSS, age, and longer stay correlated with higher mortality. Ambulance arrival and lower diastolic blood pressure and lower body mass index predicted poorer outcomes. Conclusions: This model's predictive capacity emphasizes the significance of NIHSS, age, hospital stay, arrival mode, heart rate, blood pressure, and BMI in stroke mortality prediction. Specific findings suggest avenues for data quality enhancement, registry expansion, and real-world validation. The study underscores machine learning's potential for early mortality prediction, improving risk assessment, and personalized care. The potential transformation of care delivery through robust ML predictive tools for Stroke outcomes could revolutionize patient care, allowing for personalized plans and improved preventive strategies for stroke patients. However, it is imperative to conduct prospective validation to evaluate its practical clinical effectiveness and ensure its successful adoption across various healthcare environments.http://www.sciencedirect.com/science/article/pii/S2405844024049004StrokePrognosisMortalityIschemic strokeHemorrhagic strokeMachine learning |
| spellingShingle | Ahmad A. Abujaber Ibrahem Albalkhi Yahia Imam Abdulqadir Nashwan Naveed Akhtar Ibraheem M. Alkhawaldeh Machine learning-based prognostication of mortality in stroke patients Heliyon Stroke Prognosis Mortality Ischemic stroke Hemorrhagic stroke Machine learning |
| title | Machine learning-based prognostication of mortality in stroke patients |
| title_full | Machine learning-based prognostication of mortality in stroke patients |
| title_fullStr | Machine learning-based prognostication of mortality in stroke patients |
| title_full_unstemmed | Machine learning-based prognostication of mortality in stroke patients |
| title_short | Machine learning-based prognostication of mortality in stroke patients |
| title_sort | machine learning based prognostication of mortality in stroke patients |
| topic | Stroke Prognosis Mortality Ischemic stroke Hemorrhagic stroke Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2405844024049004 |
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