Early Stage Diabetes Identification Using Machine Learning Ensemble Techniques

Diabetes or Diabetes mellitus (DM) is an illness in which patients build a high level of blood sugar due to metabolic disorders. Diabetes occurs due to the excessiveness of glucose in the human body. The proposed approach utilizes machine learning technique such as ensemble learning technique known as stacking to enhance the predictive accuracy of a classification model. The methodology involves will used dataset downloaded from kaggle website, which contain 08 attributes and 768 records. We will divide the dataset into train and test dataset. We will train the multiple diverse base models, by using various algorithms, such as Random Forest, Ada Boost and SVM will use as a base models are then engaged in generating predictions for a validation dataset. Then we will incorporate methods in ensemble-ML (such as Bagging, Boosting, and Stacking) to enhance the predictive capability. Subsequently, a meta-model exemplified by Random Forest, will train from base model predictions. The outputs of the base models used input for the mata-model, the ensemble stacking classifier creates a more reliable model. The effectiveness of this ensemble or stacking model will demonstrate through evaluating its performance on a testing dataset, resulting in an accuracy score that validates its potential to enhance predictive outcomes compared to individual models. The approach serves as a practical strategy will force the strengths of Ensemble Learning Diabetes Diagnosis (ELDD) model and achieve superior predictive performance and will predict the diabetics is present or not in both male and female. Performance of our model will measure by using performance matrices such as Accuracy, Precision, recall, F1–Score. The propose model (ELDD) will be easily incorporated in E-healthcare systems for diagnosis of Diabetics to ensure proper treatment and recovery.