<![CDATA[Diabetes is a disease described by extreme glucose measurement in the blood and can trigger an excessive number of problems likewise in the body, like the failure of internal organs, retinopathy, and neuropathy. As per the forecasts made by World Health Organization, the figure might reach roughly 642 million by 2040, and that implies one in ten might experience diabetic diseases due to various reasons such as low activity levels, unhealthy routines, and schedules, rising tension levels and so on. Many researchers in the past have explored widely on diabetes disease through AI calculations and ML algorithms. The possibility that had persuaded us to introduce a survey of different prediction models of diabetic disease is to address the diabetes issue by recognizing and coordinating the discoveries of all-important, individual examinations. In this research, we have analyzed the different prediction algorithms and techniques by different researchers that how they predict diabetic disease. Also, we have analyzed the PIMA and symptom and other datasets and how they reach their resultant accuracy by applying different classifiers. Because of non-linear, correlated, and complex structured data in the medical field, diabetic data analysis is very difficult. That’s why Ml-based algorithms have been utilized for the prediction of diabetic disease and handle a large amount of data and it needs a different approach from others at the initial stage. We emphatically suggest our review since it involves articles from different sources that will assist different specialists with different models of prediction for diabetes.]]>
