TY  - CONF
A1  - Al-Tashi, Q.
A1  - Rais, H.
A1  - Abdulkadir, S.J.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85057093093&doi=10.1109%2fICCOINS.2018.8510615&partnerID=40&md5=7eba2df7a01681929d1953ce29b02c25
PB  - Institute of Electrical and Electronics Engineers Inc.
SN  - 9781538647431
Y1  - 2018///
ID  - scholars9864
TI  - Hybrid Swarm Intelligence Algorithms with Ensemble Machine Learning for Medical Diagnosis
KW  - Ant colony optimization; Feature extraction; Learning systems; Singular value decomposition; Swarm intelligence; Wavelet transforms
KW  -  Ant colony systems; Diagnostic model; Discrete wavelets transforms; Disease diagnosis; Hybrid dynamics; Medical data sets; Swarm intelligence algorithms; Wavelets transform
KW  -  Diagnosis
N1  - cited By 28; Conference of 4th International Conference on Computer and Information Sciences, ICCOINS 2018 ; Conference Date: 13 August 2018 Through 14 August 2018; Conference Code:141665
N2  - Disease Diagnosis still an open problem in current research. The main characteristic of diseases diagnostic model is that it helps physicians to make quick decisions and minimize errors in diagnosis. Current existing techniques are not consistent with all diseases datasets. While they achieve a good accuracy on specific dataset, their performance drops on other diseases datasets. Therefore, this paper proposed a hybrid Dynamic ant colony system three update levels, with wavelets transform, and singular value decomposition integrating support vector machine. The proposed method will be evaluated using five benchmark medical datasets of various diseases from the UCI repository. The expected outcome of the proposed method seeks to minimize subset of features to attain a satisfactory disease diagnosis on a wide range of diseases with the highest accuracy, sensitivity, and specificity. © 2018 IEEE.
AV  - none
ER  -