Mining three-dimensional anthropometric body surface scanning data for hypertension detection.
Hypertension is a major disease, being one of the top ten causes of death in Taiwan. The exploration of three-dimensional (3-D) anthropometry scanning data along with other existing subject medical profiles using data mining techniques becomes an important research issue for medical decision support. This research attempts to construct a prediction model for hypertension using anthropometric body surface scanning data. This research adopts classification trees to reveal the relationship between a subject's 3-D scanning data and hypertension disease using the hybrid of the association rule algorithm (ARA) and genetic algorithms (GAs) approach. The ARA is adopted to obtain useful clues based on which the GA is able to proceed its searching tasks in a more efficient way. The proposed approach was experimented and compared with a regular genetic algorithm in predicting a subject's hypertension disease. Better computational efficiency and more accurate prediction results from the proposed approach are demonstrated.
Chiu
Chaochang
C
Department of Information Management, Yuan Ze University, Chungli 320, Taiwan, ROC. imchiu@saturn.yzu.edu.tw
Hsu
Kuang-Hung
KH
Hsu
Pei-Lun
PL
Hsu
Chi-I
CI
Lee
Po-Chi
PC
Chiou
Wen-Ko
WK
Liu
Thu-Hua
TH
Chuang
Yi-Chou
YC
Hwang
Chorng-Jer
CJ
D000465:Algorithms; D000886:Anthropometry / Q000379:methods*; D001185:Artificial Intelligence; D006801:Humans; D006973:Hypertension / Q000175:diagnosis*; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D051598:Whole Body Imaging / Q000379:methods*