Data Mining Techniques Based Diabetes Prediction
Aditya Saxena1, Megha Jain2, Prashant Shrivastava3

1Aditya Saxena, Student, Department of computer science and Engineering, Lakshmi Narain College of Technology Excellence Bhopal, India.

2Megha Jain, Assistant professor, Department of computer science and Engineering, Lakshmi Narain College of Technology Excellence Bhopal, India.

3Prashant Shrivastava, Assistant professor, Department of computer science and Engineering, Lakshmi Narain College of Technology Excellence Bhopal, India.

Manuscript received on 27 March 2021 | Revised Manuscript received on 08 April 2021 | Manuscript Accepted on 15 April 2021 | Manuscript published on 30 April 2021 | PP: 29-35 | Volume-1 Issue-2, April 2021 | Retrieval Number: 100.1/ijainn.B1012021221 | DOI: 10.54105/ijainn.B1012.041221

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Abstract: Data mining plays an important part in the healthcare sector disease prediction. Techniques of data mining are commonly used in early disease detection. Diabetes is one of the world’s greatest health challenges. A widespread chronic condition is a diabetes. Diabetes prediction is a science that is increasingly growing. Diabetes prediction at an early stage will lead to better therapy. It is necessary to avoid, monitor and increase diabetes consciousness because it causes other health issues. Diabetes of type 1 or type 2 can lead to heart disorders, kidney diseases or complications with the eye. This survey paper reflects on numerous approaches and data mining strategies used to forecast multiple diabetes disorders at an early stage. Become a chronic disease because of diabetes. The patient lives will be spared by an early prediction of this disease. By the use of data mining tools and processes, diabetes is avoided and treatment rates are reduced. The association rule mining, classification, clustering, Random Forest, Prediction as well as the Artificial Neural Network (ANN) are among the most common and important data mining technology. Different data mining methods are available to avoid diseases such as cardiac disease, cancer including kidney etc. This study examines the use of data mining methods to predict multiple disease types.

Keywords: Data Mining, Diabetes Diseases, Prediction of Diabetes, Data Mining Techniques
Scope of the Article: Supervised Learning