Smart Diabetes Risk Detection System Using Machine Learning and Health Behavior Analysis

Abstract

Diabetes is one of the most common chronic diseases worldwide, and early detection plays a crucial role in preventing severe health complications. This paper proposes an intelligent diabetes prediction system using advanced machine learning techniques to accurately identify individuals at risk. The system integrates both clinical parameters such as glucose level, blood pressure, and body mass index, along with lifestyle-related factors including physical activity and dietary habits, to improve prediction accuracy. Multiple machine learning models, including Logistic Regression, Decision Tree, and Random Forest, are implemented and evaluated to determine the most effective approach. The proposed system undergoes data preprocessing, feature selection, and model training to enhance performance and reduce error rates. Experimental results demonstrate that the system achieves high accuracy, improved precision, and reduced error rate, making it suitable for real-time healthcare applications. The user-friendly interface further enables easy interaction and accessibility for non-technical users. Overall, the proposed system provides a reliable and efficient solution for early-stage diabetes detection and supports preventive healthcare by enabling timely medical intervention.

Introduction

Diabetes is a widespread chronic disease that can lead to serious complications if not detected early. A key stage called prediabetes offers an important opportunity for prevention through lifestyle changes before the condition progresses. Traditional diagnosis relies on clinical blood tests and doctor evaluation, which can be slow and not always accessible for early detection.

To address this, the proposed work develops a machine learning-based system that predicts diabetes risk using both medical and lifestyle factors such as age, BMI, glucose levels, diet, sleep, and physical activity. Algorithms like Logistic Regression, Decision Tree, and Random Forest are used to classify individuals into risk categories (low, moderate, or high) and provide basic health recommendations.

Existing systems, including traditional clinical methods and basic ML models, either lack real-time prediction, use limited data, or do not include lifestyle factors. In contrast, the proposed system improves accuracy by combining multiple data types and automating prediction.

Conclusion

The proposed diabetes prediction system presents an efficient and reliable solution for early-stage identification of diabetes risk using advanced machine learning techniques. By combining both clinical parameters such as glucose level, BMI, and blood pressure with lifestyle-related factors including physical activity, diet patterns, and sleep habits, the system provides a more comprehensive and accurate analysis compared to traditional methods. The implementation of machine learning models such as Logistic Regression, Decision Tree, and Random Forest enables the system to learn complex patterns from the dataset and generate precise predictions. Among these, ensemble-based approaches improve overall performance by reducing overfitting and increasing model stability. The experimental evaluation confirms that the system achieves high accuracy, low error rate, and fast execution time, making it suitable for real-time applications. The use of preprocessing techniques and feature selection further enhances the efficiency and effectiveness of the model. Additionally, the system demonstrates strong generalization capability when tested on unseen data, ensuring consistent and dependable results. The user-friendly interface of the system allows individuals to easily input their data and receive clear prediction outcomes along with basic health recommendations. This reduces the dependency on complex medical procedures for initial risk assessment and promotes awareness among users regarding their health condition. Overall, the proposed system serves as a powerful tool for early diabetes detection and preventive healthcare. It not only assists users in understanding their risk level but also encourages proactive measures to maintain a healthy lifestyle. With further enhancements and real-world integration, the system has the potential to contribute significantly to digital healthcare solutions and improve the quality of life for individuals.

References

[1] A. Aljumah et al., “Application of data mining: Diabetes health care in young and old patients,” J. King Saud Univ., 2013. [2] H. Liu et al., “A comparative study of machine learning algorithms for diabetes prediction,” IEEE Access, 2019. [3] P. Kavakiotis et al., “Machine learning and data mining methods in diabetes research,” Comput. Struct. Biotechnol. J., 2017. [4] S. Perveen et al., “Performance analysis of data mining classification techniques for diabetes,” Procedia Computer Science, 2016. [5] R. Sisodia and S. Sisodia, “Prediction of diabetes using classification algorithms,” Procedia Computer Science, 2018. [6] J. Smith et al., “Using logistic regression for diabetes prediction,” Medical Informatics, 2015. [7] A. Dinh et al., “A deep learning approach for diabetes prediction,” IEEE Trans., 2020. [8] M. Kavitha and R. Chitra, “Diabetes prediction using machine learning techniques,” IJCSIT, 2019. [9] S. Priya et al., “Analysis of decision tree for diabetes prediction,” IEEE Conf., 2018. [10] V. Gupta and A. Kumar, “Random forest based diabetes prediction model,” International Journal of Engineering, 2020. [11] WHO, “Global report on diabetes,” 2016. [12] American Diabetes Association, “Standards of medical care in diabetes,” 2022. [13] UCI Repository, “PIMA Indians Diabetes Dataset,” 2019. [14] K. Polat and S. Gunes, “Artificial neural networks for diabetes detection,” Expert Systems, 2007. [15] N. Sneha and T. Gangil, “Analysis of diabetes prediction using ML,” IJARCS, 2019. [16] M. Joshi and R. Kumar, “Comparative analysis of ML algorithms for healthcare,” IEEE, 2021. [17] S. R. Basha et al., “Machine learning framework for diabetes prediction,” Springer, 2020. [18] A. Kaur and R. Kaur, “Prediction of diabetes using data mining techniques,” IJITEE, 2018. [19] M. Han et al., “Healthcare prediction using classification techniques,” IEEE Access, 2020. [20] R. Patel and M. Shah, “Data mining techniques for diabetes diagnosis,” IJCA, 2017. [21] J. Lee et al., “Clinical decision support systems for diabetes,” Health Informatics Journal, 2018. [22] S. Kumari et al., “Feature selection methods in diabetes prediction,” IEEE Conf., 2020. [23] A. Sharma et al., “Big data analytics in healthcare,” Elsevier, 2019. [24] M. Islam et al., “Diabetes prediction using supervised learning,” IEEE, 2021. [25] P. Singh et al., “Comparative study of ML algorithms for disease prediction,” Springer, 2020. [26] R. K. Sharma, “Predictive analytics in healthcare systems,” IJCS, 2018. [27] S. Mehta et al., “Risk prediction models in diabetes,” Medical Systems Journal, 2019. [28] A. Verma et al., “Classification techniques in medical diagnosis,” IEEE, 2017. [29] N. Patel et al., “Machine learning approach for chronic disease prediction,” Elsevier, 2021. [30] T. Roy et al., “Healthcare analytics using AI,” Springer, 2022. [31] K. Reddy et al., “Prediction of diabetes using logistic regression,” IJERT, 2018. [32] D. Kaur et al., “ML-based health monitoring systems,” IEEE, 2020. [33] S. Das et al., “AI in early disease detection,” IEEE Access, 2021. [34] A. Khan et al., “Decision tree analysis in medical data,” Springer, 2019. [35] M. Gupta et al., “Random forest applications in healthcare,” Elsevier, 2020. [36] R. Jain et al., “Predictive modeling for diabetes risk,” IJCA, 2019. [37] P. Nair et al., “Machine learning in clinical decision making,” IEEE, 2022. [38] S. Iyer et al., “Web-based healthcare systems using ML,” Springer, 2021. [39] V. Patel et al., “AI-powered healthcare applications,” Elsevier, 2022. [40] K. Singh et al., “Data-driven disease prediction models,” IEEE Access, 2023.

Copyright

Copyright © 2026 Ms. S. Kalaivani , S. Ashwin, G. Durga, T. Arulkumar, U. Brindha Shri. 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.