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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | Prediction of Diabetes in the early stage by using Machine Learning Algorithms |
| Authors Name: | M.Ganesh Babu , Homer Benny Bandela |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_211694
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| Published Paper Id: | IJNRD2312366 |
| Published In: | Volume 8 Issue 12, December-2023 |
| DOI: | http://doi.one/10.1729/Journal.37372 |
| Abstract: | Diabetes is a common and serious medical illness that needs to be properly and promptly diagnosed in order to be managed. In this study, we propose the optimized Bagging Classifier, a novel method for predicting cases of diabetes and non-diabetes using machine learning methods. Based on a variety of evaluation metrics, the Logistic Regression, Support Vector Machine (SVM), and Random Forest techniques are compared to the optimized Bagging Classifier's performance. With an astounding accuracy of 93%, the suggested optimized Bagging Classifier achieves outstanding results. A significant percentage of real diabetes cases are correctly identified among the projected positives with an accuracy of 81%. In order to ensure prompt medical care, the perfect recall of 100% demonstrates its capacity to record all actual incidents of diabetes. The reliability in achieving a harmonious balance between recall and precision is further supported by the balanced F1 score of 0.89. Additionally, its better capacity to discriminate between cases with and without diabetes is shown by the high AUC score of 0.94. In terms of accuracy, precision, recall, F1 score, and AUC score, the optimized Bagging Classifier surpasses the other algorithms, according to the comparison analysis. Although the other models perform admirably, recall and F1 score are weak, which could result in the missed instances of diabetes. This study emphasizes the importance of the optimized Bagging Classifier in accurately predicting diabetes and demonstrates its potential to assist medical professionals in making defensible decisions. The critical assessment emphasizes the need for more optimization and modification of the other algorithms to improve their prediction powers. Our research provides useful information about the efficacy of different machine learning algorithms for diabetes prediction, enabling better patient outcomes and healthcare administration.. |
| Keywords: | Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and Optimized Bagging Classifier (OBC) |
| Cite Article: | "Prediction of Diabetes in the early stage by using Machine Learning Algorithms", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 12, page no.d596-d605, December-2023, Available :http://www.ijnrd.org/papers/IJNRD2312366.pdf |
| Downloads: | 000118769 |
| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
| Publication Details: |
Published Paper ID:IJNRD2312366 Registration ID: 211694 Published In: Volume 8 Issue 12, December-2023 DOI (Digital Object Identifier): http://doi.one/10.1729/Journal.37372 Page No: d596-d605 Country: West Godavari, Andhra Pradesh, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2312366 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2312366 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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