INTELLIGENT SYSTEM FOR PREDICTING DIABETES AND PERSONALIZED INSULIN DOSAGE

Abstract

Elevated glucose (blood sugar) levels are a hallmark of diabetes, a metabolic disease that may last for years. Insulin, a hormone secreted by the pancreas, generally controls blood sugar levels in the body. However, high blood sugar levels result from either inadequate insulin synthesis or cells' inability to react to insulin in people with diabetes.
In order to keep blood sugar levels within a specified range, people with diabetes must take medicine, follow a certain diet, exercise regularly, and check their blood sugar levels. Complications such as cardiovascular disease, renal damage, nerve damage, and vision difficulties may arise from uncontrolled diabetes.
Linear Regression technique for insulin dose prediction in diabetic patients and Gradient Boosting Classifier for diabetes prediction. You want to train the models using the PIMA diabetes dataset and then utilise the UCI insulin dosage dataset to forecast insulin dose.
You have decided to train the Gradient Boosting Classifier on the PIMA diabetes dataset and use the UCI insulin dosage dataset to predict insulin dose. Obtain these datasets and ensure they are in the correct format for your machine learning techniques. It is possible that pre-processing the datasets is necessary prior to training the models. Data normalisation, feature separation, and addressing missing values are all possible steps in this process.
When training a Gradient Boosting Classifier, make use of the PIMA diabetes dataset. The existence of diabetes may be predicted by this algorithm by learning patterns and correlations in the data. After training the classifier, you'll want to submit a test dataset that doesn't have any class labels. Make a diabetes diagnosis prediction for every test dataset sample using the learnt model.
Using the UCI insulin dosage dataset, you can forecast the insulin dose for samples that the Gradient Boosting Classifier has predicted to have diabetes. Extract important characteristics for insulin dose prediction and preprocess the information as needed. The UCI insulin dose dataset that has been pre-processed in order to train a Linear Regression model. The model will figure out how to take insulin doses based on the characteristics fed into it.
Apply the trained Linear Regression model on the samples that the Gradient Boosting Classifier predicted to have diabetes. Each sample's insulin dose will be predicted by the model.
Compare and contrast how well the Linear Regression and Gradient Boosting Classifiers performed. One way to measure how well the models work is by looking at their accuracy, precision, recall, and mean squared error (MSE).