A Comparative Study of Decision Tree and Neural Network Algorithms for Stroke Risk Prediction

Salwa Nur JB; Lola Astri Nadita; Fachrurazy Fachrurazy; Sri Hidayati

Penulis

Salwa Nur JB Universitas Pembangunan Panca Budi Penulis
Lola Astri Nadita Universitas Pembangunan Panca Budi Penulis
Fachrurazy Fachrurazy Universitas Pembangunan Panca Budi Penulis
Sri Hidayati Universitas Pembangunan Panca Budi Penulis

Kata Kunci:

Decision Tree, Machine Learning, Neural Network , Risk Prediction, Stroke

Abstrak

Stroke is one of the leading non-communicable diseases that causes high mortality and long-term disability, making early risk prediction an important public health issue. In Indonesia, the increasing prevalence of stroke highlights the need for data-driven approaches to support early detection and prevention efforts. This study aims to compare the performance of Decision Tree and Neural Network algorithms in predicting stroke risk using health-related data. The research method employs a publicly available stroke prediction dataset obtained from Kaggle consisting of 5,111 records. Data preprocessing was conducted to handle missing values and prepare the dataset for modeling, followed by data splitting into training and testing sets using an 80:20 ratio. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. The results show that the Decision Tree model achieved higher overall accuracy of 80.72%, but demonstrated low recall for the stroke class. In contrast, the Neural Network model produced a lower accuracy of 69.37% but achieved a high recall of 82%, indicating better sensitivity in detecting stroke cases. These findings reveal a trade-off between overall accuracy and sensitivity in both models. It can be concluded that Neural Network is more suitable for stroke risk prediction when early detection is prioritized, while Decision Tree is preferable for achieving higher general classification accuracy.

Referensi

R. Ayllón-Gavilán, F. J. Martínez-Estudillo, D. Guijo-Rubio, C. Hervás-Martínez, and P. A. Gutiérrez, “Splitting criteria for ordinal decision trees: An experimental study,” arXiv preprint arXiv:2412.13697, 2024.

R. Dastres and M. Soori, “Artificial neural network systems,” International Journal of Imaging and Robotics, vol. 21, no. 2, pp. 13–25, 2021.

Dataloka, “Prevalensi stroke masyarakat Indonesia berdasarkan kelompok usia 2023,” 2023. [Online]. Available: https://dataloka.id/humaniora/3715/prevalensi-stroke-masyarakat-indonesia-berdasarkan-kelompok-usia-2023/

A. P. Erwanda, A. Khaidar, and Nurdin, “Convolutional neural network model for multi-class sentiment analysis on Twitter with deep learning approach,” Journal of Artificial Intelligence and Engineering Applications, vol. 5, no. 1, pp. 1965–1970, 2025.

M. Frasca, D. La Torre, and G. Pravettoni, “Explainable and interpretable artificial intelligence in medicine: A systematic bibliometric review,” Discover Artificial Intelligence, vol. 4, p. 15, 2024, doi: 10.1007/s44163-024-00114-7.

A. Syahputra and S. Antoni, “Performance analysis of classification algorithms in decision support systems for early detection of chronic diseases,” Journal of Technology and Computer (JOTECHCOM), vol. 2, no. 1, pp. 42–46, 2025. [Online]. Available: https://journal.technolabs.co.id/index.php/jotechcom/article/view/4

T.-T. Huynh-Cam, L.-S. Chen, and H. Le, “Using decision trees and random forest algorithms to predict and determine factors contributing to first-year university students’ learning performance,” Algorithms, vol. 14, no. 11, p. 318, 2021, doi: 10.3390/a14110318.

Katadata Insight Center, “Be aware! This age group is at high risk of stroke,” 2024. [Online]. Available: https://databoks.katadata.co.id/kesehatan/statistik/waspada-kelompok-usia-ini-berisiko-tinggi-terkena-stroke

J. Morgan, Suhartina, and S. L. R. Nasution, “Prevalence of stroke in type II diabetes mellitus patients at Royal Prima Ayahanda Hospital in 2022–2024,” Syntax Literate: Indonesian Scientific Journal, vol. 10, no. 11, 2025, doi: 10.36418/syntax-literate.v10i11.62516.

M. O. Okwu and L. K. Tartibu, “Artificial neural network,” in Metaheuristic Optimization: Nature-Inspired Algorithms, Swarm and Computational Intelligence, Theory and Applications, pp. 133–145, 2021.

A. D. Purwanto, K. Wikantika, A. Deliar, and S. Darmawan, “Decision tree and random forest classification algorithms for mangrove forest mapping in Sembilang National Park, Indonesia,” Remote Sensing, vol. 15, no. 1, p. 16, 2023, doi: 10.3390/rs15010016.

I. Z. Sadiq, B. S. K. Sanusi, B. Ibrahim, et al., “Data-driven diabetes mellitus prediction and management: A comparative evaluation of decision tree classifier and artificial neural network models,” Scientific Reports, vol. 15, p. 19339, 2025, doi: 10.1038/s41598-025-03718-w.

S. Smith and S. McConnell, “The use of artificial neural networks and decision trees: Implications for health care research,” Open Computer Science, vol. 14, no. 1, 2024, doi: 10.1515/comp-2022-0279.

N. Susanti and P. A. Maulana, “Relationship of comorbid disease with the incidence of stroke in the elderly group,” Jurnal Ilmu Kesehatan Masyarakat (IKESMA), vol. 19, no. 4, 2025, doi: 10.19184/ikesma.v19i4.43506.

R. Susanti, P. Oktamianti, and F. D. Permata, “Training related to non-communicable diseases: Cancer, heart disease, stroke, and uronephrology,” Journal of Nursing, vol. 9, no. 2, pp. 2010–2017, 2025, doi: 10.31004/jn.v9i2.42518.

A. S. Upoyo, Y. Sari, and W. Ekowati, “Peer group education for stroke prevention in hypertension groups in Susukan Village, Sumbang District, Banyumas Regency,” Journal of Community Health, vol. 4, no. 1, pp. 60–70, 2023.