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All Journal Proceeding International Conference Of Innovation Science, Technology, Education, Children And Health Proceeding International Conference Of Innovation Science, Technology, Education, Children And Health
Valiant Shabri Rabbani
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Computer Science & IT Education Engineering Public Health Social Sciences Other

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Stroke Disease Prediction Using Support Vector Machine Method Gayatri Dwi Santika; Valiant Shabri Rabbani
Proceeding International Conference Of Innovation Science, Technology, Education, Children And Health Vol. 5 No. 1 (2025): Proceeding of The International Conference of Inovation, Science, Technology, E
Publisher : Program Studi DIII Rekam Medis dan Informasi Kesehatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/icistech.v5i1.274

Abstract

Stroke is one of the leading causes of death globally and is particularly prevalent in Indonesia. Early prediction of stroke is critical to reducing the risk of long-term disability and mortality. This study aims to build a stroke prediction model using the Support Vector Machine (SVM) classification method. The dataset used is sourced from Kaggle, containing 5,110 records with class imbalance. To address the imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied during preprocessing. The study evaluates model performance across multiple data splits (70:30, 80:20, 90:10) and k-fold cross-validation values (k=5, 7, 10). The SVM was tested with various kernel types—linear, polynomial, and radial basis function (RBF)—along with parameter tuning for C, gamma, and degree. The results show that the polynomial kernel yielded the highest prediction accuracy of 92%. The model performance was evaluated using accuracy, precision, recall, and F1-score metrics.
Stroke Disease Prediction Using Support Vector Machine Method Gayatri Dwi Santika; Valiant Shabri Rabbani
Proceeding International Conference Of Innovation Science, Technology, Education, Children And Health Vol. 5 No. 1 (2025): Proceeding of The International Conference of Inovation, Science, Technology, E
Publisher : Program Studi DIII Rekam Medis dan Informasi Kesehatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/icistech.v5i1.274

Abstract

Stroke is one of the leading causes of death globally and is particularly prevalent in Indonesia. Early prediction of stroke is critical to reducing the risk of long-term disability and mortality. This study aims to build a stroke prediction model using the Support Vector Machine (SVM) classification method. The dataset used is sourced from Kaggle, containing 5,110 records with class imbalance. To address the imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied during preprocessing. The study evaluates model performance across multiple data splits (70:30, 80:20, 90:10) and k-fold cross-validation values (k=5, 7, 10). The SVM was tested with various kernel types—linear, polynomial, and radial basis function (RBF)—along with parameter tuning for C, gamma, and degree. The results show that the polynomial kernel yielded the highest prediction accuracy of 92%. The model performance was evaluated using accuracy, precision, recall, and F1-score metrics.
Co-Authors Gayatri Dwi Santika