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A case-based classifier for hypertension detection
Authors:Kuang-Hung Hsu  Chaochang Chiu  Nan-Hsing Chiu  Po-Chi Lee  Wen-Ko Chiu  Thu-Hua Liu  Chorng-Jer Hwang
Affiliation:1. Dept. of Health Care Management, Chang Gung University, Taiwan, ROC;2. Dept. of Information Management, Yuan Ze University, Taiwan, ROC;3. Dept. of Information Management, Ching Yun University, Taiwan, ROC;4. Dept. of Industrial Design, Chang Gung University, Taiwan, ROC;1. University of Novi Sad, Faculty of Technical Sciences, Trg Dositeja Obradovi?a 6, 21000 Novi Sad, Serbia;2. University of Novi Sad, Medical Faculty, Department of Anatomy, Hajduk Veljkova 3, 21000 Novi Sad, Serbia;3. University of Novi Sad, Medical Faculty, Department of Endocrinology, Diabetes and Metabolic Disorders, Hajduk Veljkova 1, 21000 Novi Sad, Serbia;1. Department of Chemistry, Moscow State University, 119991 Moscow, Russia;2. Max-Planck-Institute for Solid State Research, Heisenbergstrasse 1, 70569 Stuttgart, Germany;1. Ernst Abbe University of Applied Science, Carl Zeiss Promenade 2, 07745 Jena, Germany;2. TU Ilmenau, Institute of Physics, Dept. of Experimental Physics I, P/O Box 10 05 65, 98684 Ilmenau, Germany;3. Innovent e. V., Prüssingstr. 27 B, 07745 Jena, Germany;1. Department of Computer Science, Edge Hill University, UK;2. IBM Research, Dublin Lab, Ireland;3. Department of Computer Science, University of Salerno, Italy;1. Master in Health Science, Federal University of Sergipe, Aracaju, Brazil;2. University Hospital, Federal University of Sergipe, Aracaju, Brazil;3. Department of Statistics, Federal University of Sergipe, Aracaju, Brazil;4. Department of Paediatrics, Federal University of São Paulo, São Paulo, Brazil;5. Department of Medicine, Federal University of Sergipe, Aracaju, Brazil
Abstract:The exploration of three-dimensional (3D) 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 classification approach based on the hybrid use of case-based reasoning (CBR) and genetic algorithms (GAs) for hypertension detection using anthropometric body surface scanning data. The obtained result reveals the relationship between a subject’s 3D scanning data and hypertension disease. The GA is adopted to determine the appropriate feature weights for CBR. The proposed approaches were experimented and compared with a regular CBR and other widely used approaches including neural nets and decision trees. The experiment showed that applying GA to determine the suitable weights in CBR is a feasible approach to improving the effectiveness of case matching of hypertension disease. It also demonstrated that different weighted CBR approach presents better classification accuracy over the results obtained from other approaches.
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