Average Weighted Objective Distance-Based Method for Type 2 Diabetes Prediction

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.

Recent applications of machine learning and deep learning models in the prediction, diagnosis, and management of diabetes: a comprehensive review

“…A method called average-based weighted objective distance (AWOD) was developed for the prediction of T2DM [ 71 ]. This approach uses information based on average amounts of the expected amounts and acceptable levels to prioritize factors naming as weighing factors.…”

Weighted Voting Ensemble for Depressive Disorder Analysis with Multi-objective Optimization

“…P(A), P(B) is the likelihood of event A or B. K-nearest neighbors (KNN) is a simple classifying method that involves scrutinizing the closest distance between the data and the learning set, where k is the number of the nearest neighbors. K should be an odd number to avoid the same number of label neighbors problems [35]. The KNN method compares all the features between the unknown and the training dataset then considers the rows closest to the classified rows measured by a distance function such as Euclidean Manhattan and Minkowski.…”

Una revisión de las implementaciones de sistemas para la identificación de tendencias de la diabetes

“…Una de las herramientas más utilizadas es la técnica de ML, la cual permite que los sistemas inteligentes construyan modelos apropiados mediante el aprendizaje y la extracción de patrones en los datos (Li et al, 2020), pues es necesario que un conjunto de datos apropiados tenga las características necesarias para el respectivo entrenamiento y validación (Ahmed et al, 2022). Entre las aplicaciones se encuentran sistemas para la predicción de la diabetes tipo 2, que utilizan el promedio ponderado objetivo basado en la distancia, cuyos resultados son prometedores debido a su método de predicción novedoso (Nuankaew et al, 2021). Otras aplicaciones con mejores resultados emplean más de una técnica de aprendizaje automático, integrando la lógica difusa como método de construcción (Ahmed et al, 2022).…”

“…Son un tipo de redes neuronales artificiales muy efectivas para tareas de visión artificial, como la clasificación y segmentación de imágenes, entre otras aplicaciones. (Nuankaew et al, 2021) Distancia objetiva ponderada promedio (AWOD)…”

“…Wang et al, 2019) Algoritmo CART, algoritmo DMP_MI CART es un algoritmo de clasificación y árboles de regresión ampliamente utilizado en análisis predictivo. (Jaiswal et al, 2021), (Woldaregay et al, 2019), (Nuankaew et al, 2021), (Zimmerman et al, 2021), (Hong et al, 2020) Support vector machine (SVM)…”