Predictive Modelling of Stroke Occurrence among Patients using Machine Learning
Stroke is a global public health concern with severe consequences. Early detection and accurate prediction of stroke occurrence are crucial for effective prevention and targeted interventions. This study proposes a machine learning-based approach to predict the likelihood of stroke among patie...
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| Format: | Article |
| Language: | English English |
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INTI International University
2023
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| Online Access: | http://eprints.intimal.edu.my/1811/ http://eprints.intimal.edu.my/1811/1/ij2023_55r.pdf http://eprints.intimal.edu.my/1811/2/120 |
| _version_ | 1848766963090718720 |
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| author | Sures, Narayasamy Thilagamalar, Maniam |
| author_facet | Sures, Narayasamy Thilagamalar, Maniam |
| author_sort | Sures, Narayasamy |
| building | INTI Institutional Repository |
| collection | Online Access |
| description | Stroke is a global public health concern with severe consequences. Early detection and accurate
prediction of stroke occurrence are crucial for effective prevention and targeted interventions. This
study proposes a machine learning-based approach to predict the likelihood of stroke among
patients. A comprehensive dataset encompassing demographic, clinical, and lifestyle factors of a
large patient cohort was employed. Variables such as age, gender, hypertension, diabetes, smoking
status, BMI, and medical history were considered. Advanced machine learning algorithms,
including logistic regression, decision trees, random forests, and support vector machines, were
utilized to analyses the dataset and develop a predictive model. The results demonstrate that the
machine learning-based approach achieved high predictive accuracy in identifying individuals at
risk of stroke. The model exhibited excellent sensitivity and specificity, enabling effective
stratification of patients based on their stroke likelihood. Developing an accurate stroke prediction
model using machine learning holds immense potential for proactive healthcare strategies and
personalized patient care. Early identification of high-risk patients enables timely intervention and
implementation of preventive measures, potentially reducing the burden of stroke-related
complications. This study showed that the supervised K-Nearest Neighbors Algorithm (K-NN)
model outperforms the other methods, with an accuracy of 95% compared with other models. |
| first_indexed | 2025-11-14T11:57:33Z |
| format | Article |
| id | intimal-1811 |
| institution | INTI International University |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-14T11:59:29Z |
| publishDate | 2023 |
| publisher | INTI International University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | intimal-18112025-07-24T06:54:51Z http://eprints.intimal.edu.my/1811/ Predictive Modelling of Stroke Occurrence among Patients using Machine Learning Sures, Narayasamy Thilagamalar, Maniam Q Science (General) QP Physiology Stroke is a global public health concern with severe consequences. Early detection and accurate prediction of stroke occurrence are crucial for effective prevention and targeted interventions. This study proposes a machine learning-based approach to predict the likelihood of stroke among patients. A comprehensive dataset encompassing demographic, clinical, and lifestyle factors of a large patient cohort was employed. Variables such as age, gender, hypertension, diabetes, smoking status, BMI, and medical history were considered. Advanced machine learning algorithms, including logistic regression, decision trees, random forests, and support vector machines, were utilized to analyses the dataset and develop a predictive model. The results demonstrate that the machine learning-based approach achieved high predictive accuracy in identifying individuals at risk of stroke. The model exhibited excellent sensitivity and specificity, enabling effective stratification of patients based on their stroke likelihood. Developing an accurate stroke prediction model using machine learning holds immense potential for proactive healthcare strategies and personalized patient care. Early identification of high-risk patients enables timely intervention and implementation of preventive measures, potentially reducing the burden of stroke-related complications. This study showed that the supervised K-Nearest Neighbors Algorithm (K-NN) model outperforms the other methods, with an accuracy of 95% compared with other models. INTI International University 2023-11 Article PeerReviewed text en cc_by_4 http://eprints.intimal.edu.my/1811/1/ij2023_55r.pdf text en cc_by_4 http://eprints.intimal.edu.my/1811/2/120 Sures, Narayasamy and Thilagamalar, Maniam (2023) Predictive Modelling of Stroke Occurrence among Patients using Machine Learning. INTI JOURNAL, 2023 (55). pp. 1-6. ISSN e2600-7320 https://intijournal.intimal.edu.my |
| spellingShingle | Q Science (General) QP Physiology Sures, Narayasamy Thilagamalar, Maniam Predictive Modelling of Stroke Occurrence among Patients using Machine Learning |
| title | Predictive Modelling of Stroke Occurrence among Patients using Machine Learning |
| title_full | Predictive Modelling of Stroke Occurrence among Patients using Machine Learning |
| title_fullStr | Predictive Modelling of Stroke Occurrence among Patients using Machine Learning |
| title_full_unstemmed | Predictive Modelling of Stroke Occurrence among Patients using Machine Learning |
| title_short | Predictive Modelling of Stroke Occurrence among Patients using Machine Learning |
| title_sort | predictive modelling of stroke occurrence among patients using machine learning |
| topic | Q Science (General) QP Physiology |
| url | http://eprints.intimal.edu.my/1811/ http://eprints.intimal.edu.my/1811/ http://eprints.intimal.edu.my/1811/1/ij2023_55r.pdf http://eprints.intimal.edu.my/1811/2/120 |