Review Paper on Diabetes Prediction

Abstract

Millions of people all over the world suffer from diabetes, a chronic disease that requires early checkups and efficient treatment. Clinical diagnosis techniques depend on clinical assessments and blood testing, which can be expensive and time-consuming for everyone. Predictive models have become effective instruments for early detection of diabetes as a result of developments in artificial intelligence and machine learning. This study analyzes several machine learning methods for diabetes prediction, such as logistic regression, decision trees, support vector machines, and deep learning. It also examines datasets that are widely used, including real-time health monitoring systems and the Pima Indian Diabetes Dataset (originally from the National Institute of Diabetes and Digestive and Kidney Diseases).This review addresses various challenges such as data privacy, inaccurate models, and lack of result interpretabilityIt also emphasizes the need for integrating these predefined predictive models into clinical approaches to enhance patient outcomes and make the process more efficient. Feature selection, data imbalance, and model clarity are also discussed while focusing on the models\' advantages and disadvantages. Enhancing real-world applicability, incorporating data from wearable gadgets, and increasing model accuracy are suggested as future directions. This study aims to provide a clearer understanding of the current landscape and contribute to more efficient and accurate solutions in diabetes prediction.

Introduction

Diabetes, characterized by high blood glucose levels, is a growing global health issue. Traditional diagnostic methods (e.g., glucose, BMI, insulin levels) often detect diabetes late, leading to severe complications. Advances in Artificial Intelligence (AI) and Machine Learning (ML) enable early diabetes prediction by analyzing large datasets to uncover hidden risk patterns. Wearable devices and real-time monitoring further support proactive management.

Research has employed various ML algorithms—such as logistic regression, support vector machines, decision trees, and especially XGBoost, a powerful ensemble method—to improve prediction accuracy. The widely used Pima Indian Diabetes Dataset serves as a benchmark, although its limited demographic scope calls for more diverse data for better model generalization.

The study implemented a machine learning pipeline using Python and tools like Pandas, Scikit-learn, and XGBoost to preprocess data, train models, and evaluate performance. XGBoost outperformed simpler models like logistic regression due to its ability to capture complex patterns and avoid overfitting.

While results are promising, future improvements should focus on incorporating diverse datasets and real-time data from wearables. Overall, ML, particularly ensemble approaches, shows strong potential to enhance early diabetes detection and support preventive healthcare.

Conclusion

This study explored the use of various machine learning models for predicting diabetes based on health-related features such as glucose level, BMI, and blood pressure. Among all models evaluated, XGBoost outperformed others by delivering high accuracy and better handling of non-linear patterns and overfitting. Logistic Regression, while simpler, provided a solid baseline and remains useful for its interpretability. The use of machine learning pipelines helped streamline the development process by ensuring clean, consistent data preprocessing and training. Evaluation metrics such as accuracy, F1-score, and confusion matrix further confirmed the models\' reliability and performance. However, the study also identified limitations, particularly in the use of the Pima Indian Diabetes Dataset, which lacks diversity and may affect the generalizability of results. For future improvements, more inclusive and real-time datasets — such as those from wearable devices — should be used to enhance prediction accuracy and relevance in real-world scenarios. In conclusion, machine learning holds strong potential in transforming diabetes detection and management. With further enhancement in model explainability and dataset quality, these predictive systems can be effectively integrated into clinical workflows to support early diagnosis and preventive care.

References

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Copyright

Copyright © 2025 Gaurav Singh, Anika Bisht, Harsh Srivastav, Bhumika Verma, Rahul Chaurasiya. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.