Different Techniques Used For Predicting Diabetes -Literature Survey

computer engineering

Diabetes is a life-threatening disease that has no cure. If this disease strikes you once, it will be with you for the rest of your life. At the same time, having too much glucose in your blood might cause health problems. Kidney illness, heart disease, stroke, vision, dental, foot, and nerve damage are just a few examples. As a result, you can take steps to manage your Diabetes and avoid complications. Type 1 and type 2 diabetes are the two most common types of Diabetes. This type of Diabetes causes issues such as the body's inability to manufacture or utilize insulin. However, other types of Diabetes, such as gestational diabetes, can develop during pregnancy. High blood sugar levels caused by gestational Diabetes can harm your pregnancy and your baby's health. To diagnose and administer Diabetes, a variety of machine learning and data mining technologies are applied. This research focuses on recent advances in machine learning that have had a substantial influence on diabetes detection and diagnosis. Machine learning techniques are employed to classify diabetic patients in this study. The accuracy of categorization is attained by classifying diabetic patients. Diabetes is one of India's significant health issues nowadays. It's a set of syndromes that cause blood sugar levels to rise too high. It's a long-term disorder that disrupts the body's blood sugar control mechanisms. Diabetes mellitus prevention and prediction are becoming more popular in medical research. The purpose of this research is to conduct a survey of the various diabetes prediction strategies currently in use.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.