Diabetes Prediction Using Different Machine Learning Techniques

Registration ID: IJNRD_222602

Published ID: IJNRD2405731

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Rahul Kumar Sachdeva, Prakhar Kumar Singh, Rahul Lodhi, Anam Khan

Machine Learning, SVM, KNN, Naive Bayes, Gradient Boosting Classifier, Random Forest Algorithm.

Diabetes mellitus, particularly type-2 diabetes, represents a substantial portion of global diabetes cases, exerting significant pressure on healthcare systems worldwide[1]. This metabolic disorder, marked by inadequate insulin production or response leading to heightened blood sugar levels, is linked with numerous health complications, including heart and kidney diseases. Conventional diagnosis involves frequent visits to diagnostic centers, consuming both time and financial resources. However, the advent of machine learning technologies offers a promising solution to this challenge. By leveraging advanced data processing techniques, machine learning models can predict the onset of diabetes, enabling early intervention and improved patient outcomes. This research aims to support physicians in the timely identification and effective diagnosis of type 2 diabetes. Supervised machine learning techniques were executed to “Pima dataset”, utilizing six predictors to develop predictive models. The study employs classification algorithms such as SVM, KNN, Naive Bayes, Gradient Boosting Classifier, Logistic Regression, and Random Forest. Results indicate promising accuracy levels across the models, with Support Vector Machine achieving 76%, KNN 80%, Naive Bayes 76%, Gradient Boosting Classifier 85%, Logistic Regression 80%, and Random Forest 96%. These outcomes underscore the efficacy of machine learning approaches in diabetes prediction, offering a valuable tool for healthcare professionals to enhance diagnosis and patient care. This study advances the creation of accurate and effective type 2 diabetes diagnosis tools by utilizing machine learning's predictive capabilities. The findings highlight the potential of machine learning algorithms to analyze large volumes of diabetes-related data, enabling proactive healthcare interventions and ultimately improving patient outcomes. Moreover, the study underscores the importance of ongoing research and confirmation efforts to guarantee the dependability and effectiveness of machine learning in clinical settings.

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Rahul Kumar Sachdeva, Prakhar Kumar Singh, Rahul Lodhi, & Anam Khan (May-2024). Diabetes Prediction Using Different Machine Learning Techniques. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(5), h104-h108. https://ijnrd.org/papers/IJNRD2405731.pdf

Volume 9 Issue 5, May-2024

Pages : h104-h108

Paper Reg. ID: IJNRD_222602

Published Paper Id: IJNRD2405731

Research Area: Information Technology 

Author Type: Indian Author

Country: Greater Noida, Uttar Pradesh, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2405731.pdf

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