From Data to Prediction: Comparative Analysis of Machine Learning Classifiers for Type 2 Diabetes

Abstract

imely identification and diagnosis of medical conditions hold paramount im- portance in averting severe health complications and optimizing healthcare effica-cy. Machine Learning, an offshoot of Artificial Intelligence, possesses considera-ble potential in anticipatory analysis through the integration of Data Mining. The objective of our investigation is to establish a streamlined mechanism for the prompt and precise identification of Type 2 diabetes by utilizing the widely rec-ognized Pima dataset, which encompasses eight clinical parameters. To ensure equitable consideration of all features, we employ the "Standard scaler" technique for feature scaling