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 | Machine Learning for Diabetes Prediction |
|---|---|
| ABSTRACT | Diabetes is one of the most common chronic health conditions affecting a large number of people worldwide, leading to serious health complications if detected and treated at a late stage. Conventional approaches for detecting diabetes include clinical tests and expert opinions, which are often time-consuming. Moreover, it is not always possible to ensure early detection. With recent advances in technology, machine learning has been found to be an effective method for predicting diabetes by processing large amounts of medical data and identifying underlying patterns and factors. This paper aims to provide a comprehensive review of machine learning-based approaches for predicting diabetes, as reported in various studies between 2020 and 2025. Supervised learning approaches, including Logistic Regression, Support Vector Machines, Decision Tree, Random Forest, and various ensemble-based approaches, have been used to improve the accuracy of prediction. The approaches are based on various parameters, including glucose levels, body mass index, age, blood pressure, and family history of diabetes. The recent developments include the integration of deep learning and hybrid approaches for improving prediction accuracy. However, challenges such as data imbalance, lack of quality datasets, overfitting, and model interpretability persist. The study demonstrates the prospect of machine learning in facilitating early diagnosis and preventive healthcare for diabetes patients. Future work in this area should be directed at creating precise, interpretable, and scalable machine learning models for practical applications in healthcare. |
| AUTHOR | M. KRISHNA KUMAR, P. SAMAYAN, S. G. ABARNA, S.VARSHA, P.MEENADHARSHINI Assistant Professor, Department of Mathematics, AAA College of Engineering and Technology, Sivakasi, Tamil Nadu, India Assistant Professor, Department of Artificial Intelligence and Data Science, AAA College of Engineering and Technology, Sivakasi, Tamil Nadu, India UG Student, Department of Artificial Intelligence and Data Science, AAA College of Engineering and Technology, Sivakasi, Tamil Nadu, India |
| VOLUME | 182 |
| DOI | DOI: 10.15680/IJIRCCE. 2026.1403091 |
| pdf/91_Machine Learning for Diabetes Prediction.pdf | |
| KEYWORDS | |
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