Data mining for the diagnosis of type II diabetes from three-dimensional body surface anthropometrical scanning data

Authors:
Chad-Ton Su;Chien-Hsin Yang;Kuang-Hung Hsu;Wen-Ko Chiu
Affiliations:
-;-;-;-
Venue:
Computers & Mathematics with Applications
Year:
2006

Citing 8
Cited 10

Analysis of notions of diagnosis

Artificial Intelligence
Data mining: concepts and techniques

Data mining: concepts and techniques
Artificial neural networks compared to factor analysis for low-dimensional classification of high-dimensional body fat topography data of healthy and diabetic subjects

Computers and Biomedical Research
Induction of Decision Trees

Machine Learning
Paper: On using feedforward neural networks for clinical diagnostic tasks

Artificial Intelligence in Medicine
Self-organizing map for cluster analysis of a breast cancer database

Artificial Intelligence in Medicine
Comparison of neural network predictors in the classification of tracheal-bronchial breath sounds by respiratory auscultation

Artificial Intelligence in Medicine
Extracting rules from trained neural networks

IEEE Transactions on Neural Networks

Feature selection and classification model construction on type 2 diabetic patients' data

Artificial Intelligence in Medicine
Using SVM based method for equipment fault detection in a thermal power plant

Computers in Industry
Study of diabetes mellitus (DM) with ophthalmic complication using association rules of data mining technique

ICCCI'11 Proceedings of the Third international conference on Computational collective intelligence: technologies and applications - Volume Part I
Fuzzy logistic regression based on the least squares approach with application in clinical studies

Computers & Mathematics with Applications
Dosage planning for type 2 diabetes mellitus patients using Indexing HDMR

Expert Systems with Applications: An International Journal
Application of classification techniques on development an early-warning system for chronic illnesses

Expert Systems with Applications: An International Journal
Self-organizing maps for translating health care knowledge: a case study in diabetes management

AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
Intelligent fault prediction system based on internet of things

Computers & Mathematics with Applications
Comparison of NN and LR classifiers in the context of screening native American elders with diabetes

Expert Systems with Applications: An International Journal
Review: Knowledge discovery in medicine: Current issue and future trend

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.09

Visualization

Abstract

Diabetes mellitus has become a general chronic disease as a result of changes in customary diets. Impaired fasting glucose (IFG) and fasting plasma glucose (FPG) levels are two of the indices which physicians use to diagnose diabetes mellitus. Although this is a fairly accurate approach, the tests are expensive and time consuming. This study attempts to construct a prediction model for Type II diabetes using anthropometrical body surface scanning data. Four data mining approaches, including backpropagation neural network, decision tree, logistic regression, and rough set, were used to select the relevant features from the data to predict diabetes. Accuracy of classification was evaluated for these approaches. The result showed that volume of trunk, left thigh circumference, right thigh circumference, waist circumference, volume of right leg, and subjects' age were associated with the condition of diabetes. The accuracy of the classification of decision tree and rough set was found to be superior to that of logistic regression and backpropagation neural network. Several rules were then extracted based on the anthropometrical data using decision tree. The result of implementing this method is not only useful for the physician as a tool for diagnosing diabetes, but it is sophisticated enough to be used in the practice of preventive medicine.