Type 2 diabetes is a persistent disorder that affects millions of individuals globally. It is characterised by the excessive levels of glucose within the blood due to insulin resistance or the incapability to supply insulin. Early detection and prediction of type 2 diabetes can improve patient outcomes. K-Nearest Neighbor (KNN) is used in the present model to predict type 2 diabetes. The KNN set of rules is a simple but powerful machine learning set of rules used for categorization and regression. It's far a non-parametric approach that makes predictions based totally on the nearest k-neighbours in a dataset. KNN is widely used in healthcare and scientific studies to expect and classify sicknesses primarily based on the affected person’s data. The intention of this work is to predict the threat of growing type 2 diabetes using the KNN set of rules. Data has been collected from electronic medical records of patients diagnosed with type 2 diabetes and healthy individuals. The dataset consists of various patient attributes, such as age, gender, body mass index, blood pressure, cholesterol levels, and glucose levels. Information has also been collected about lifestyle habits, such as physical activity, smoking status, and alcohol consumption. Data have been pre-processed by removing missing values and outliers, and normalization of the data has been done to ensure that all features have the same scale. Splitting the dataset into training and test sets, with training sets using 80% of the data and test sets using 20% of the data is performed. KNN algorithm have been used to classify the patients into two groups: those at high risk of developing type 2 diabetes and those at low risk. The model's performance has been assessed using a variety of metrics, including accuracy, precision, recall, and F1-score.

Prediction F1 Recall K Fold Confusion Matrix