Abstract
Early detection of Type 2 diabetes is necessary for its prevention. The prediction models for detection systems normally employ common factors that may not properly fit all persons having different health conditions. Therefore, this study proposes a method for type 2 diabetes prediction with factors representing personal health conditions. More specifically, this study proposes a novel prediction method named Average Weighted Objective Distance (AWOD) based on the assumption that the individual has diverse health conditions resulting from different individual factors, a requirement for an effective prediction model. AWOD is a modification of Weighted Objective Distance (WOD) by applying information gain to reveal significant and insignificant individual factors having different priorities, which are represented by different weights. For AWOD, the data set is divided into a training set used to determine all relevant thresholds and constant values required for AWOD calculation and the testing set. In particular, AWOD is designed for binary classification problems with a relatively small dataset. To validate the proposed method, two datasets from open sources, Pima Indians Diabetes (Dataset 1) and Mendeley Data for Diabetes (Dataset 2) each containing 392 records, were studied. The prediction performance for both datasets is compared with the machine learning-based prediction methods, including K-Nearest Neighbors, Support Vector Machines, Random Forest, and Deep Learning. The comparison results showed that the proposed method provided 93.22% and 98.95% accuracy for Dataset 1 and Dataset 2, respectively, which are higher than those provided by other machine learning-based methods.
Highlights
Diabetes, formally called diabetes mellitus, is a group of abnormal metabolic and chronic diseases
To evaluate the prediction accuracy of the Average Weighted Objective Distance (AWOD) based method, the result was compared with the observed value for each data sample
A constant value was used to calculate the AWOD value representing real effects towards the prediction based on the number of both significant and insignificant factors, and a factor with the
Summary
Formally called diabetes mellitus, is a group of abnormal metabolic and chronic diseases. It causes elevated blood glucose levels, which results in prolonged high blood sugar levels [1]. Diabetes mellitus can cause many other complications and serious negative side effects, up to and including diabetic ketoacidosis, nonketotic hyperosmolar, heart disease, stroke, kidney failure, foot ulcers, vision loss, and blindness [2,3]. According to the International Diabetes Federation, approximately 463 million people had diabetes worldwide in 2019 This amount is expected to increase by 51% in the 26 years with around 700 million people living with diabetes worldwide in 2045 [5]. Detection and treatment of diabetes is a major step forward in necessary treatment for diabetic patients, which can reduce the risk of serious complications [6]
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