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
