JACIII Vol.24 No.1 pp. 73-82
doi: 10.20965/jaciii.2020.p0073


A Kernel k-Means-Based Method and Attribute Selections for Diabetes Diagnosis

Tru Cao*,**, Chau Vo*, Son Nguyen*, Atsushi Inoue**, and Duanning Zhou**

*Ho Chi Minh City University of Technology, Vietnam National University
268 Ly Thuong Kiet Street, District 10, Ho Chi Minh City, Vietnam

**Eastern Washington University
668 North Riverpoint Boulevard, Spokane, Washington 99202-1677, USA

December 11, 2018
September 18, 2019
January 20, 2020
machine learning, clustering, PIMA dataset, MIMIC database, medical decision support system

Diabetes diagnosis is important due to the high death rate and complication consequences caused by the disease. First, we propose a kernel k-means-based prediction method and explore attribute selections for effective and robust diabetes diagnosis. This method derives homogeneous sub-clusters in the high dimensional kernelized feature space to compute the distance of a new instance to those sub-clusters, and then apply the 1-nearest neighbor to classify it as positive or negative to the disease. Our experimental results could identify the best effective attribute group for each considered prediction method and show that the proposed method outperforms the existing ones for the task. Second, we introduce our developed diabetes visualization and decision support system, named DIAVIS, which is equipped with the proposed prediction method. This system can support doctors to diagnose diabetes and track patient health progress to prescribe proper medications in a treatment process.

Overview architecture of DIAVIS system

Overview architecture of DIAVIS system

Cite this article as:
T. Cao, C. Vo, S. Nguyen, A. Inoue, and D. Zhou, “A Kernel k-Means-Based Method and Attribute Selections for Diabetes Diagnosis,” J. Adv. Comput. Intell. Intell. Inform., Vol.24 No.1, pp. 73-82, 2020.
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Last updated on May. 10, 2024