Andere Kunden interessierten sich auch für
![Improvement the prediction using unsupervised discretization method]( "Improvement the prediction using unsupervised discretization method")
Improvement the prediction using unsupervised discretization method26,99 €![Data Warehousing and Data Mining]( "Data Warehousing and Data Mining")
Data Warehousing and Data Mining40,99 €![Prediction Theory: The Case of Image Information Preserving]( "Prediction Theory: The Case of Image Information Preserving")
Prediction Theory: The Case of Image Information Preserving38,99 €![Identification of Handwritten Characters using Machine Learning]( "Identification of Handwritten Characters using Machine Learning")
Identification of Handwritten Characters using Machine Learning26,99 €![Signature Analysis and Trait Prediction]( "Signature Analysis and Trait Prediction")
Signature Analysis and Trait Prediction40,99 €![Classification Problem in Data Mining Using Decision Trees]( "Classification Problem in Data Mining Using Decision Trees")
Classification Problem in Data Mining Using Decision Trees36,99 €![Machine Learning Based Android Malware Detection]( "Machine Learning Based Android Malware Detection")
Machine Learning Based Android Malware Detection40,99 €
The aim of this work is to raise patient's awareness by estimating the blood sugar levels in order to avoid miscalculations/misinterpretations of blood sugar measurements due to human error. In order make a prediction on the patient's blood sugar levels, the medication levels, food consumption and post blood sugar measurements of several patients are used as the dataset. An extensive set of features is extracted from this dataset in order to create a blood sugar level corpus, which is used to train a machine learning model for predicting the future blood sugar measurements. These models are then tested on the same patient set, for evaluating the performance of the proposed technique. In this work, a wide range of new contributions is also provided such as comparison of different machine learning algorithms with respect to their prediction quality and analysis on a large range of attributes for predicting blood sugar levels such as insulin dose, food consumption and activity rates.