Comparative Analysis of KNN and Decision Tree Classification Algorithms for Early Stroke Prediction: A Machine Learning Approach

  • Karin Eldora Universitas Multimedia Nusantara, Indonesia
  • Erick Fernando Universitas Multimedia Nusantara, Indonesia
  • Winanti Winanti Universitas Insan Pembangunan, Indonesia
Keywords: Early Stroke, Prediction, Machine Learning, Comparison Algorithms, Classification, KNN, Decision Tree

Abstract

Stroke is the second most deadly disease in the world and the third leading cause of disability. However, most deaths due to stroke can be prevented by recognizing the symptoms of stroke and taking preventive measures using information technology. Therefore, this research utilizes the role of information technology using a machine learning approach to predict stroke in a person using the K-Nearest Neighbor and Decision Tree classification methods. The two algorithms were compared to determine which algorithm was more effective in predicting stroke. Data analysis using the CRISP-DM approach was carried out using a dataset containing 5110 observations with 12 relevant attributes. Implementation of Exploratory Data Analysis (EDA) was also carried out for preprocessing, and oversampling techniques were applied to overcome the problem of unbalanced classes. The research results show that the predictive model with the highest level of accuracy was obtained at around 97.1845% using the K-Nearest Neighbor algorithm. This research makes a significant contribution to stroke prevention efforts through the use of information technology and machine learning algorithms for early identification of stroke risk.

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References

V. Plotnikova, M. Dumas, and F. Milani, “Adaptations of data mining methodologies: A systematic literature review,” PeerJ Comput. Sci., vol. 6, pp. 1–43, 2020, doi: 10.7717/PEERJ-CS.267.

F. Martinez-Plumed et al., “CRISP-DM Twenty Years Later: From Data Mining Processes to Data Science Trajectories,” IEEE Trans. Knowl. Data Eng., vol. 33, no. 8, pp. 3048–3061, 2021, doi: 10.1109/TKDE.2019.2962680.

J. S. Saltz and I. Krasteva, “Current approaches for executing big data science projects—a systematic literature review,” PeerJ Comput. Sci., vol. 8, pp. 1–24, 2022, doi: 10.7717/PEERJ-CS.862.

D. Singh and B. Singh, “Investigating the impact of data normalization on classification performance,” Appl. Soft Comput., vol. 97, p. 105524, Dec. 2020, doi: 10.1016/j.asoc.2019.105524.

M. Kubat, An Introduction to Machine Learning. 2021. doi: 10.1007/978-3-030-81935-4.

D. Chopra and R. Khurana, Introduction to Machine Learning with Python. Singapore: Bentham Science, 2023.

X.-S. Yang, Introduction to Algorithms for Data Mining and Machine Learning. Candice Janco, 2019.

G. Sailasya and G. L. A. Kumari, “Analyzing the Performance of Stroke Prediction using ML Classification Algorithms,” Int. J. Adv. Comput. Sci. Appl., vol. 12, no. 6, pp. 539–545, 2021, doi: 10.14569/IJACSA.2021.0120662.

M. Daidone, S. Ferrantelli, A. Tuttolomondo, M. Daidone, and M. Daidone, “Machine learning applications in stroke medicine: Advancements, challenges, and future prospective,” Neural Regen. Res., vol. 19, no. 4, pp. 769–773, 2024, doi: 10.4103/1673-5374.382228.

M. S. Sirsat, E. Fermé, and J. Câmara, “Machine Learning for Brain Stroke: A Review,” J. Stroke Cerebrovasc. Dis., vol. 29, no. 10, 2020, doi: 10.1016/j.jstrokecerebrovasdis.2020.105162.

D. Chicco and G. Jurman, “The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation,” BMC Genomics, vol. 21, no. 1, pp. 1–13, 2020, doi: 10.1186/s12864-019-6413-7.

A. Viloria, O. B. Pineda Lezama, and N. Mercado-Caruzo, “Unbalanced data processing using oversampling: Machine Learning,” Procedia Comput. Sci., vol. 175, pp. 108–113, 2020, doi: 10.1016/j.procs.2020.07.018.

C. Fernandez-Lozano et al., “Random forest-based prediction of stroke outcome,” Sci. Rep., vol. 11, no. 1, pp. 1–12, 2021, doi: 10.1038/s41598-021-89434-7.

S. K. Kwak and J. H. Kim, “Statistical data preparation: management of missing values and outliers,” Korean J. Anesthesiol., vol. 70, no. 4, p. 407, 2017, doi: 10.4097/kjae.2017.70.4.407.

T. Al-Shehari and R. A. Alsowail, “An Insider Data Leakage Detection Using One-Hot Encoding, Synthetic Minority Oversampling and Machine Learning Techniques,” Entropy, vol. 23, no. 10, p. 1258, Sep. 2021, doi: 10.3390/e23101258.

F. Thabtah, S. Hammoud, F. Kamalov, and A. Gonsalves, “Data imbalance in classification: Experimental evaluation,” Inf. Sci. (Ny)., vol. 513, pp. 429–441, Mar. 2020, doi: 10.1016/j.ins.2019.11.004.

B. Charbuty and A. Abdulazeez, “Classification Based on Decision Tree Algorithm for Machine Learning,” J. Appl. Sci. Technol. Trends, vol. 2, no. 01, pp. 20–28, Mar. 2021, doi: 10.38094/jastt20165.

Published
2024-03-25
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How to Cite
Eldora, K., Fernando, E., & Winanti, W. (2024). Comparative Analysis of KNN and Decision Tree Classification Algorithms for Early Stroke Prediction: A Machine Learning Approach. Journal of Information Systems and Informatics, 6(1), 313-338. https://doi.org/10.51519/journalisi.v6i1.664