International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Diabetes Prediction Using Machine Learning Approaches |
|---|---|
| ABSTRACT | Diabetes mellitus is a chronic metabolic disorder affecting over 537 million adults worldwide. Timely detection of diabetes risk is critical for early medical intervention. This paper presents an integrated diabetes prediction and dietary monitoring system deployed as a Flask web application. A Random Forest Classifier trained on the PIMA Indian Diabetes Dataset (768 records, 8 features) achieves 82.03% accuracy and an AUC-ROC of 0.862. A MobileNetV2 CNN trained on 200 food images across four categories achieves 84.38% training accuracy with a confidence-based fallback using a pretrained ImageNet MobileNetV2 for predictions below 0.70. The application provides secure authentication, automated BMI computation, personalized health tips, dietary recommendations, and PDF report generation. All nine functional test cases passed end-to-end validation, confirming system integrity. |
| AUTHOR | RAMYA K, HEERA SHINY V, MARIAMMAL M, LEGA M, J. REVATHY Department of Artificial Intelligence and Data Science, Christ the King Engineering College, Coimbatore, Tamil Nadu, India Project Guide, Department of Artificial Intelligence and Data Science, Christ the King Engineering College, Coimbatore, Tamil Nadu, India |
| VOLUME | 184 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1405034 |
| pdf/34_Diabetes Prediction Using Machine Learning Approaches.pdf | |
| KEYWORDS | |
| References | [1] IDF, “Diabetes Atlas,” 10th ed., International Diabetes Federation, Brussels, Belgium, 2021. [2] R. Priya and P. Aruna, “SVM and Neural Network Based Diagnosis of Diabetic Retinopathy,” Int. J. Computer Applications, vol. 41, no. 1, pp. 6–12, 2013. [3] V. Subramanian, S. Sriram and R. Lavanya, “Transfer Learning Based South Indian Food Classification Using MobileNetV2,” Int. J. Advanced Computer Science and Applications, vol. 11, no. 9, pp. 423–430, 2020. [4] N. Sneha and T. Gangil, “Analysis of Diabetes Mellitus for Early Prediction using Optimal Features Selection,” J. Big Data, vol. 6, no. 1, pp. 1–19, 2019. [5] D. Sisodia and D. S. Sisodia, “Prediction of Diabetes using Classification Algorithms,” Procedia Computer Science, vol. 132, pp. 1578–1585, 2018. [6] A. Mujumdar and V. Vaidehi, “Diabetes Disease Prediction using ML on Big Data with Apache Spark,” Procedia Computer Science, vol. 165, pp. 23–38, 2019. [7] H. Kaur and V. Kumari, “Predictive Modelling and Analytics for Diabetes using a ML Approach,” Applied Computing and Informatics, vol. 18, no. 1/2, pp. 90–100, 2018. [8] S. I. Ayon and Md. M. Islam, “Diabetes Prediction: A Deep Learning Approach,” Int. J. Information Engineering and Electronic Business, vol. 11, no. 2, pp. 21–27, 2019. [9] T. Zhu, K. Li, P. Herrero and P. Georgiou, “Deep Learning for Diabetes: A Systematic Review,” IEEE J. Biomedical and Health Informatics, vol. 25, no. 7, pp. 2744–2757, 2021. [10] L. Bossard, M. Guillaumin and L. Van Gool, “Food-101 — Mining Discriminative Components with Random Forests,” in Proc. ECCV, Zurich, pp. 446–461, 2014. [11] C. Liu et al., “DeepFood: Deep Learning-Based Food Image Recognition for Computer-Aided Dietary Assessment,” in Proc. Int. Conf. Inclusive Smart Cities and Digital Health, Springer, pp. 37–48, 2016. [12] S. Dey, C. Bhatt and A. S. Ashour, “Big Data for Remote Sensing: Visualization, Analysis and Interpretation,” Springer, Cham, pp. 13–35, 2021. [13] R. Ahuja et al., “Classification and Clustering Algorithms of ML with their Applications,” Machine Learning and Big Data Analysis, Springer, Singapore, pp. 225–248, 2019. [14] Q. Zou et al., “Predicting Diabetes Mellitus with Machine Learning Techniques,” Frontiers in Genetics, vol. 9, Article 515, pp. 1–10, 2018. |
