Please use this identifier to cite or link to this item: http://localhost:8080/xmlui/handle/123456789/7765
Title: Models for predicting the quality of life domains on the general population through the orange data mining approach
Authors: Bustanul Arifin, Bustanul Arifin
Dyah Aryani Perwitasari, Dyah Aryani Perwitasari
Zulkarnain Zulkarnain, Zulkarnain Zulkarnain
M Rifqi Rokhman, M Rifqi Rokhman
Keywords: Prediction
quality of life
data mining
artificial intelligence
Issue Date: Mar-2022
Publisher: Pharmaciana
Abstract: The incidence of type 2 diabetes mellitus (DM) has been predicted to increase until 2045 in the world. Furthermore, long-term treatment and lifestyle factors affect the quality of life. This study aims to determine the models that can be used to predict the quality-of-life domains in general population by using Artificial Intelligent (AI) devices. This is a cross-sectional design in which the inclusion criteria were individuals of age above 18 years and has never been diagnosed with diabetes mellitus (both type 1 DM and type 2 DM), fasted for at least 8 hours, and are willing to sign an informed consent after having received an explanation. Participants were asked to fill out two questionnaires, namely the Indonesian version of the Finnish Diabetes Risk Score (FINDRISC) and the EuroQoL-5 Dimensions-5 Level (EQ-5D-5L). The AI application uses OrangeĀ® machine learning with three models used in predictive analysis, such as Logistic Regression, Neural Network, and SVM. In addition, the model was evaluated using the sensitivity, precision, and accuracy of the AU-ROC parameters. The results showed that the neural network model based on the AUC value, precision, accuracy, and also the ROC analysis, was the best for predicting the utility index of domains in the EQ-5D-5L questionnaire, based on demographic data and the FINDRISC questionnaire.
URI: http://localhost:8080/xmlui/handle/123456789/7765
ISSN: 2088 4559
Appears in Collections:VOL 12 NO 1 2022

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