article · International Journal of Intelligent Systems
A computerised system for predicting diabetes uses machine learning techniques evaluated on both the Pima Indians dataset and private patient datasets. Addressing data class imbalance through the SMOTE technique, the approach assesses ten distinct classification models, including logistic regression, random forest, and support vector machines. The combination of an XGBoost algorithm with SMOTE achieved the highest performance, yielding an accuracy of 97.4 percent on the private dataset and 83.1 percent on combined datasets. To make predictions interpretable, the system incorporates explainable artificial intelligence through SHAP methods, alongside domain adaptation techniques to ensure flexibility across different data environments. To make early screening widely accessible, the system is implemented within a mobile application, enabling instant diabetes risk prediction based on user-entered health features to support early disease detection and proactive management.
Early identification of diabetes is essential to avoid long-term health complications. By pairing high-performing machine learning with explainable artificial intelligence and delivering the capability through a mobile application, individuals can receive instant risk assessments based on personal health metrics. This enables earlier medical intervention and supports public health efforts to curb rising diabetes rates.
The research presents an applied and tested solution featuring a functional mobile application designed for direct end-user diabetes risk screening. Potential users include individuals seeking personal health monitoring and healthcare providers requiring preliminary screening tools. Given the integration of explainable artificial intelligence and a deployed mobile interface, the technology appears relatively close to real-world deployment, though further clinical validation on broader populations may precede formal commercial rollout.
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With the increasing prevalence of diabetes in Saudi Arabia, there is a critical need for early detection and prediction of the disease to prevent long-term health complications. This study addresses this need by using machine learning (ML) techniques applied to the Pima Indians dataset and private diabetes datasets through the implementation of a computerized system for predicting diabetes. In contrast to prior research, this study employs a semisupervised model combined with strong gradient boosting, effectively predicting diabetes-related features of the dataset. Additionally, the researchers employ the SMOTE technique to deal with the problem of imbalanced classes. Ten ML classification techniques, including logistic regression, random forest, KNN, decision tree, bagging, AdaBoost, XGBoost, voting, SVM, and Naive Bayes, are evaluated to determine the algorithm that produces the most accurate diabetes prediction. The proposed approach has achieved impressive performance. For the private dataset, the XGBoost algorithm with SMOTE achieved an accuracy of 97.4%, an F1 coefficient of 0.95, and an AUC of 0.87. For the combined datasets, it achieved an accuracy of 83.1%, an F1 coefficient of 0.76, and an AUC of 0.85. To understand how the model predicts the final results, an explainable AI technique using SHAP methods is implemented. Furthermore, the study demonstrates the adaptability of the proposed system by applying a domain adaptation method. To further enhance accessibility, a mobile app has been developed for instant diabetes prediction based on user-entered features. This study contributes novel insights and techniques to the field of ML-based diabetic prediction, potentially aiding in the early detection and management of diabetes in Saudi Arabia.
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DOI: 10.1155/2024/6688934
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