Cutting-Edge Machine Learning Methods for Diabetes Forecasting

Registration ID: IJNRD_225750

Published ID: IJNRD2407509

DOI: http://doi.one/10.1729/Journal.40856

V.Vishnu priya , Pragatheeswari E , Nisanth G , Dhanushree D , Sivakumar P

Keywords - Diabetes- KNN (K-Nearest Neighbor)- SVM (Support Vector Machine)- Hyperparameter- Grid Search- Machine Learning- Naive Bayes- Decision Tree- Random Forest- Gradient Boosting Machines (GBM)- Neural Networks- K-Means Clustering- Hierarchical Clustering- Principal Component Analysis (PCA)- Singular Value Decomposition (SVD)- Logistic Regression.

: Diabetes is a dangerous medical condition that can cause heart disease, renal problems, visual problems, and other complications. For these problems to be effectively managed and prevented, early diabetes prediction is essential. Using patient data, machine learning algorithms present a viable method for diabetes prediction. Support Vector Machine and K-Nearest Neighbor with Grid Search Optimization are two particularly effective methods that routinely yield very accurate predictions, properly detecting diabetes in about 99 out of 100 cases. Further useful techniques for diabetes prediction include Gradient Boosting Machines, Neural Networks, Principal Component Analysis, Logistic Regression, Singular Value Decomposition, K-Means Clustering, Hierarchical Clustering, and Gaussian Mixture Models. The Random Forest technique is another efficient approach that achieves comparable high accuracy rates of approximately 99% by combining numerous decision trees. By combining these algorithms with Random Forest, we can detect and treat diabetes early on, improving patient outcomes and prediction accuracy.

Published Paper   E-Certificate

V.Vishnu priya, Pragatheeswari E, Nisanth G, Dhanushree D, & Sivakumar P (July-2024). Cutting-Edge Machine Learning Methods for Diabetes Forecasting . INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(7), g97-g114. http://doi.one/10.1729/Journal.40856

Volume 9 Issue 7, July-2024

Pages : g97-g114

Paper Reg. ID: IJNRD_225750

Published Paper Id: IJNRD2407509

Downloads: 000121976

Research Area: Science & Technology

Country: Knageyam,tirupur, Tamilnadu, India

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Crossref DOI: http://doi.one/10.1729/Journal.40856

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

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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