Early Risk Prediction of Diabetes Categorization Using Fuzzy K-Means Clustering Algorithm


  • R. Sangeethapriya Assistant professor, Department of Information Technology, Sona college of Technology, Salem 636 005, Tamil Nadu
  • S. Gomathi Assistant Professor, Department of Computer Science and Engineering, Dr. N.G.P Institute of Technology, Coimbatore 641048, Tamil Nadu.
  • B. Dhiyanesh Associate Professor, Department of Computer Science and Engineering, Dr. N.G.P Institute of Technology, Coimbatore 641048, Tamil Nadu
  • J. K. Kiruthika Assistant Professor (Sr.G), Department of Computer Science and Engineering, KPR Institute of Engineering and Technology, Coimbatore 641407, Tamil Nadu
  • P. Saraswathi Assistant Professor, Department of Information Technology, Velammal College of Engineering and Technology, Madurai 625009, Tamil Nadu.
  • K. Divya Assistant Professor, Department of Electrical and Electronics Engineering, Karpagam Institute of Technology, Coimbatore 641021, Tamil Nadu.


Feature Selection, Classification, Clustering, Fuzzy Rules, Machine learning, diabetes


Diabetes mellitus (DM) is a metabolic disease that primarily results in high blood glucose levels. There are distinct clinical types, with Type 1 and Type 2 being the most common forms of diabetes. A significant increase has been observed in the number of young people suffering from type 1 diabetes over the past few years for this reason. Diabetes can become chronic with a long latency period in childhood and adolescence, as the symptoms in the early stages can be vague. This can make timely detection and treatment complex, possibly leading to delayed treatment. It is important to detect or prevent diabetes early. It can cause many complications, and the prediction of diabetes is not accurate for further analysis using previous methods. We introduced the new proposed method using learning (ML) approaches to overcome the issues. Based on the Fuzzy K-means Clustering and Support Vector Machine (FKMC-SVM) for deciding the classification model for diabetic prediction using a standard dataset, for an accurate result,  Initially collected, the diabetic dataset is from the standard repository, and the second step is pre-processing to reduce the imbalanced data, normalizing the values from the dataset using Z-Score normalization, and then selecting the features based on the margin values using Threshold Recursive Feature Elimination (TRFE) to eliminate the values from the pre-processing dataset based on the maximum threshold values of the recursive features in the dataset. Then the fuzzy-based method is used to decide diabetes using fuzzy logic to create interpretable models and to diagnose diabetes early based on these classifiers, FKMC and SVM, and to design fuzzy rules. FKMC refers to a collection of data points where the points in one location share similarities or connections but differ from those in another cluster. Additionally, optimizing support vector machines with larger datasets may provide more accurate results and predict the likelihood of diabetes in both Type 1 and Type 2. This combined algorithm, F-KMC-SVM, compares the precision, accuracy, recall, and F1-score of different ML techniques used to classify diabetic patients.


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How to Cite

Sangeethapriya, R. ., Gomathi, S. ., Dhiyanesh, B. ., Kiruthika, J. K. ., Saraswathi, P. ., & Divya, K. . (2024). Early Risk Prediction of Diabetes Categorization Using Fuzzy K-Means Clustering Algorithm. International Journal of Intelligent Systems and Applications in Engineering, 12(20s), 423–431. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5154



Research Article