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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
Issue per Year : 12
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Issue Published : 118
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| Paper Title: | Diabetes Prediction using Random Forest Classifier: A Machine Learning Approach |
| Authors Name: | Arun Prasad Arunachalam Sivagurulingam , Sukanthi Nachimuthu , Pothana Kandaswami Vediyappan , Lipika Vijayakumar |
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IJRTI_205356
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| Published Paper Id: | IJRTI2507092 |
| Published In: | Volume 10 Issue 7, July-2025 |
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| Abstract: | Diabetes mellitus is a chronic metabolic disorder that affects the body’s ability to produce or respond to insulin, leading to abnormal metabolism of carbohydrates and elevated blood glucose levels. It remains a major global health challenge, with the number of affected individuals expected to rise significantly in the coming decades. Early detection is crucial to preventing the onset of severe complications, including cardiovascular diseases, kidney failure, and neuropathy. In recent years, machine learning techniques have shown great promise in supporting medical diagnosis through pattern recognition and predictive analytics. In this research, we propose a Random Forest Classifier (RFC)-based model for the prediction of diabetes using a publicly available dataset from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). Our approach involves data preprocessing, feature scaling, model training, and performance evaluation using key metrics such as accuracy, precision, recall, and F1-score. The model achieved an overall prediction accuracy of 72.73%, indicating the potential of Random Forest algorithms in augmenting clinical decision-making. This study underscores the importance of integrating AI-based tools into healthcare systems to enhance the early diagnosis of diabetes and improve patient outcomes. |
| Keywords: | Diabetes Mellitus, Random Forest Classifier, Machine Learning, Early Diagnosis, Predictive Analytics, Clinical Decision Support. |
| Cite Article: | "Diabetes Prediction using Random Forest Classifier: A Machine Learning Approach", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 7, page no.a852-a861, July-2025, Available :http://www.ijrti.org/papers/IJRTI2507092.pdf |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2507092
Registration ID:205356
Published In: Volume 10 Issue 7, July-2025
DOI (Digital Object Identifier):
Page No: a852-a861 Country: Coimbatore, Tamilnadu, India Research Area: Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2507092 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2507092 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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