• Patel Ruchika

Diabetes is one of the fastest increasing chronic diseases, affecting millions of people worldwide. Its diagnosis, prognosis, appropriate treatment, and administration are essential. 382 million people worldwide have diabetes, according to the International Diabetes Federation. This will double to 592 million by 2035. Diabetes is a condition brought on by elevated blood glucose levels. The symptoms of this elevated blood sugar level include frequent urination, increased thirst, and increased hunger. Diabetes is one of the leading causes of blindness, kidney failure, amputations, heart failure and stroke. When we eat, our body turns food into sugars, or glucose. At that point, our pancreas is supposed to release insulin. Insulin serves as a key to open our cells, to allow the glucose to enter and allow us to use the glucose for energy. But with diabetes, this system does not work. Type 1 and type 2 diabetes are the most common forms of the disease, but there are also other kinds, such as gestational diabetes, which occurs during pregnancy, as well as other forms. Data mining-based forecasting strategies for diabetes data analysis can aid in the early detection and prediction of the condition. For the diagnosis, prediction, and classification of diabetes, numerous approaches have been developed. The goal of this project is to create a system that, by merging the findings of several data mining approaches, can more accurately perform early diabetes prediction for a patient. A decision tree with the techniques Naive Bayes, K Nearest Neighbour, Logistic Regression, Artificial Neural Network, and Support Vector Machine are employed. Each algorithm's accuracy is calculated along with the model's accuracy. The model for predicting diabetes is then chosen from those with good accuracy. We also emphasise the difficulties and directions for future research.

Diabetes, Data mining, Classification, Cluster, Dataset, Diagnosis, Weka, SMO, KNN, ANN, SVM

"Diabetes Prediction using Data Mining Techniques - A Comparative Study", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 6, page no.j818-j824, June-2023, Available :http://www.jetir.org/papers/JETIR2306998.pdf

"Diabetes Prediction using Data Mining Techniques - A Comparative Study", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 6, page no. ppj818-j824, June-2023, Available at : http://www.jetir.org/papers/JETIR2306998.pdf