Prediction of Diabetes in the early stage by using Machine Learning Algorithms

Prediction of Diabetes in the early stage by using Machine Learning Algorithms

Registration ID: IJNRD_211694

Published ID: IJNRD2312366

DOI: http://doi.one/10.1729/Journal.37372

M.Ganesh Babu , Homer Benny Bandela

Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and Optimized Bagging Classifier (OBC)

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..

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"Prediction of Diabetes in the early stage by using Machine Learning Algorithms", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.8, Issue 12, page no.d596-d605, December-2023, Available :https://ijnrd.org/papers/IJNRD2312366.pdf

Volume 8 Issue 12, December-2023

Pages : d596-d605

Paper Reg. ID: IJNRD_211694

Published Paper Id: IJNRD2312366

Downloads: 000121185

Research Area: Computer Science & Technology 

Country: West Godavari, Andhra Pradesh, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2312366.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2312366

DOI: http://doi.one/10.1729/Journal.37372

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

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

Publisher: IJNRD (IJ Publication) Janvi Wave

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition

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