A Comparative Study of Naive Bayes, SVM, and Decision Tree Algorithms
for Diabetes Detection Based on Health Datasets
Dublin Core
Title
A Comparative Study of Naive Bayes, SVM, and Decision Tree Algorithms
for Diabetes Detection Based on Health Datasets
for Diabetes Detection Based on Health Datasets
Subject
Machine Learning, Decision Tree, Naive Bayes, SVM, Classification, Health, Diabetes
Description
Diabetes is a chronic, progressive condition whose global prevalence continues to rise, creating substantial public health and economic burdens.
Early diagnosis and timely intervention are critical to preventing severe complications and improving long-term patient outcomes. In recent years,
artificial intelligence (AI) particularly machine learning (ML) has emerged as a powerful tool in medical diagnostics, offering capabilities in
automated pattern recognition and disease classification. This study aims to evaluate and compare the predictive performance of three supervised
ML algorithms such as Naïve Bayes, Support Vector Machine (SVM), and Decision Tree for classifying and predicting diabetes based on two
primary physiological indicators: glucose level and blood pressure. The dataset employed was sourced from Kaggle, comprising 995 patient
records containing relevant clinical attributes. The research methodology involved several stages, including data preprocessing to ensure quality
and consistency, data partitioning into training and testing subsets using an 80:20 split ratio, model training, and performance evaluation. Each
algorithm’s effectiveness was measured using accuracy, precision, recall, and F1-score metrics. The experimental findings demonstrate that the
Decision Tree algorithm achieved the highest classification accuracy (94.47%), outperforming SVM and Naïve Bayes, both of which recorded
92.96% accuracy. Moreover, the Decision Tree exhibited balanced precision and recall values, underscoring its robustness in identifying both
diabetic and non-diabetic cases with minimal misclassification. These outcomes indicate that the Decision Tree model provides an optimal
balance between predictive accuracy and interpretability, making it particularly suitable for clinical decision-support applications.
Early diagnosis and timely intervention are critical to preventing severe complications and improving long-term patient outcomes. In recent years,
artificial intelligence (AI) particularly machine learning (ML) has emerged as a powerful tool in medical diagnostics, offering capabilities in
automated pattern recognition and disease classification. This study aims to evaluate and compare the predictive performance of three supervised
ML algorithms such as Naïve Bayes, Support Vector Machine (SVM), and Decision Tree for classifying and predicting diabetes based on two
primary physiological indicators: glucose level and blood pressure. The dataset employed was sourced from Kaggle, comprising 995 patient
records containing relevant clinical attributes. The research methodology involved several stages, including data preprocessing to ensure quality
and consistency, data partitioning into training and testing subsets using an 80:20 split ratio, model training, and performance evaluation. Each
algorithm’s effectiveness was measured using accuracy, precision, recall, and F1-score metrics. The experimental findings demonstrate that the
Decision Tree algorithm achieved the highest classification accuracy (94.47%), outperforming SVM and Naïve Bayes, both of which recorded
92.96% accuracy. Moreover, the Decision Tree exhibited balanced precision and recall values, underscoring its robustness in identifying both
diabetic and non-diabetic cases with minimal misclassification. These outcomes indicate that the Decision Tree model provides an optimal
balance between predictive accuracy and interpretability, making it particularly suitable for clinical decision-support applications.
Creator
Satria Dwi Nurwicaksana1,*
, Lee Kyung Oh2
, Husni Teja Sukmana3
, Lee Kyung Oh2
, Husni Teja Sukmana3
Source
https://ijiis.org/index.php/IJIIS/article/view/230/153
Publisher
Universitas Amikom Purwokerto, Indonesia
2Sun Moon University Asan, Republic of Korea,
2Sun Moon University Asan, Republic of Korea,
Date
desember 2024
Contributor
Fajar bagus W
Format
PDF
Language
English
Type
Text
Files
Collection
Citation
Satria Dwi Nurwicaksana1,*
, Lee Kyung Oh2
, Husni Teja Sukmana3, “A Comparative Study of Naive Bayes, SVM, and Decision Tree Algorithms
for Diabetes Detection Based on Health Datasets,” Repository Horizon University Indonesia, accessed January 2, 2026, https://repository.horizon.ac.id/items/show/9721.
for Diabetes Detection Based on Health Datasets,” Repository Horizon University Indonesia, accessed January 2, 2026, https://repository.horizon.ac.id/items/show/9721.