Interpretable Diabetes Prediction using XAI in Healthcare Application
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Published
Jun 30, 2023
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Abstract
Diabetes is a chronic metabolic disorder affecting millions of people worldwide. This study investigates the application of Explainable Artificial Intelligence (XAI) techniques in the prediction and classification of diabetes using a diabetes disease dataset. The effectiveness of various machine learning algorithms such as KNN, Naive Bayes, SVM, Decision Tree, Random Forest Logistic Regression and all models in the Lazy Classifier package are investigated with XAI methods to develop accurate and interpretable models for diabetes prediction. The diabetes dataset used in the study has 768 rows and 9 columns consisting of various medical variables (independent variables) and an outcome variable (dependent variable). Hyperparameters and grid search were utilized for model optimization. The success performances of the models were evaluated with metrics such as F1 Score, accuracy, balanced accuracy, precision, recall, ROC AUC and time taken. SVM and Random Forest stand out as the most successful models. With the most successful models, the impact of different features on diabetes prediction was evaluated with different SHAP plots representing the contribution of each feature to the final prediction compared to the average prediction. Glucose, Age and BMI were found to have a significant and positive effect on the model output. The study aims to uncover important characteristics and patterns that contribute to diabetes risk using XAI and to assist healthcare professionals in providing timely intervention and personalised treatment plans.
Article Details
How to Cite
UYSAL, Ilhan.
Interpretable Diabetes Prediction using XAI in Healthcare Application.
Journal of Multidisciplinary Developments, [S.l.], v. 8, n. 1, p. 20-38, june 2023.
ISSN 2564-6095.
Available at: <http://www.jomude.com/index.php/jomude/article/view/109>. Date accessed: 21 jan. 2025.
Section
Natural Sciences - Regular Research Paper
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References
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[12] Kahn M. Diabetes. UCI Machine Learning Repository. https://doi.org/10.24432/C5T59G.
[2] Zou, Q., Qu, K., Luo, Y., Yin, D., Ju, Y., Tang, H. (2018). Predicting diabetes mellitus with machine learning techniques. Frontiers in genetics, 9, 515.
[3] Aelgani, V., Gupta, S.K., Narayana, V.A. (2023). Local Agnostic Interpretable Model for Diabetes Prediction with Explanations Using XAI. In: Reddy, K.A., Devi, B.R., George, B., Raju, K.S., Sellathurai, M. (eds) Proceedings of Fourth International Conference on Computer and Communication Technologies. Lecture Notes in Networks and Systems, vol 606. Springer, Singapore. https://doi.org/10.1007/978-981-19-8563-8_40.
[4] Soni, M., Varma, S. (2020). Diabetes prediction using machine learning techniques. International Journal of Engineering Research & Technology (Ijert) Volume, 9.
[5] Lai, H., Huang, H., Keshavjee, K., Guergachi, A., Gao, X. (2019). Predictive models for diabetes mellitus using machine learning techniques. BMC endocrine disorders, 19, 1-9.
[6] Ghosh, P., Azam, S., Karim, A., Hassan, M., Roy, K., Jonkman, M. (2021). A comparative study of different machine learning tools in detecting diabetes. Procedia Computer Science, 192, 467-477.
[7] Hasan, M. K., Alam, M. A., Das, D., Hossain, E., Hasan, M. (2020). Diabetes prediction using ensembling of different machine learning classifiers. IEEE Access, 8, 76516-76531.
[8] Kavakiotis, I., Tsave, O., Salifoglou, A., Maglaveras, N., Vlahavas, I., Chouvarda, I. (2017). Machine learning and data mining methods in diabetes research. Computational and structural biotechnology journal, 15, 104-116.
[9] Lu, H., Uddin, S. (2022). Explainable Stacking-Based Model for Predicting Hospital Readmission for Diabetic Patients. Information, 13(9), 436.
[10] Vishwarupe, V., Joshi, P. M., Mathias, N., Maheshwari, S., Mhaisalkar, S., Pawar, V. (2022). Explainable AI and Interpretable machine learning: A Case Study in Perspective. Procedia Computer Science, 204, 869-876.
[11] Nagaraj, P., Muneeswaran, V., Dharanidharan, A., Balananthanan, K., Arunkumar, M., Rajkumar, C. (2022, April). A Prediction and Recommendation System for Diabetes Mellitus using XAI-based Lime Explainer. In 2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS) (pp. 1472-1478). IEEE.
[12] Kahn M. Diabetes. UCI Machine Learning Repository. https://doi.org/10.24432/C5T59G.