This paper is published in Volume-9, Issue-1, 2023
Area
Machine Learning
Author
Manyam Kishore Kumar Reddy, Gandlapati Bhavani, Mulasthanam Venkata Moulika, Addepalli Tharun Raju, Y Mahanandi
Org/Univ
Annamacharya Institue of Technology and Science, Rajampet, Andhra Pradesh, India
Keywords
Logistic Regression, SVM, ANN, and ML Techniques
Citations
IEEE
Manyam Kishore Kumar Reddy, Gandlapati Bhavani, Mulasthanam Venkata Moulika, Addepalli Tharun Raju, Y Mahanandi. A fused machine learning technique for diabetes prediction., International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.
APA
Manyam Kishore Kumar Reddy, Gandlapati Bhavani, Mulasthanam Venkata Moulika, Addepalli Tharun Raju, Y Mahanandi (2023). A fused machine learning technique for diabetes prediction.. International Journal of Advance Research, Ideas and Innovations in Technology, 9(1) www.IJARIIT.com.
MLA
Manyam Kishore Kumar Reddy, Gandlapati Bhavani, Mulasthanam Venkata Moulika, Addepalli Tharun Raju, Y Mahanandi. "A fused machine learning technique for diabetes prediction.." International Journal of Advance Research, Ideas and Innovations in Technology 9.1 (2023). www.IJARIIT.com.
Manyam Kishore Kumar Reddy, Gandlapati Bhavani, Mulasthanam Venkata Moulika, Addepalli Tharun Raju, Y Mahanandi. A fused machine learning technique for diabetes prediction., International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.
APA
Manyam Kishore Kumar Reddy, Gandlapati Bhavani, Mulasthanam Venkata Moulika, Addepalli Tharun Raju, Y Mahanandi (2023). A fused machine learning technique for diabetes prediction.. International Journal of Advance Research, Ideas and Innovations in Technology, 9(1) www.IJARIIT.com.
MLA
Manyam Kishore Kumar Reddy, Gandlapati Bhavani, Mulasthanam Venkata Moulika, Addepalli Tharun Raju, Y Mahanandi. "A fused machine learning technique for diabetes prediction.." International Journal of Advance Research, Ideas and Innovations in Technology 9.1 (2023). www.IJARIIT.com.
Abstract
Early disease diagnosis and prevention are crucial in the medical field. One of the world's most hazardous diseases is diabetes. Sugar and fat are commonly found in modern lifestyles. Our eating behaviors, which has elevated the risk of diabetes. It is crucial to comprehend the disease's signs in order to predict it. Machine learning (ML) techniques are useful at the moment for disease identification. The model for predicting diabetes in this paper uses a fused machine-learning technique.
