A performance evaluation of systematic analysis for combining multi-class models for sickle cell disorder data sets

Mohammed Khalaf, Abir Jaafar Hussain, Dhiya Al-Jumeily, Robert Keight, Russell Keenan, Ala S. Al Kafri, Carl Chalmers, Paul Fergus, Ibrahim Olatunji Idowu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Machine learning approach is considered as a field of science aiming specifically to extract knowledge from the data sets. The main aim of this study is to provide a sophisticate model to difference applications of machine learning models for medically related problems. We attempt for classifying the amount of medications for each patient with Sickle Cell disorder. We present a new technique to combine two classifiers between the Levenberg-Marquartdt training algorithm and the k-nearest neighbours algorithm. In this paper, we introduce multi-class label classification problem in order to obtain training and testing methods for each models along with other performance evaluations. In machine learning, the models utilise a training sets in association with building a classifier that provide a reliable classification. This research discusses different aspects of machine learning approaches for the classification of biomedical data. We are mainly focus on the multi-class label classification problem where many number of classes are available in the data sets. Results have indicated that for the machine learning models tested, the combination classifiers were found to yield considerably better results over the range of performance measures that been selected for this research.
Original languageEnglish
Title of host publication Intelligent Computing Theories and Application
EditorsDe-Shuang Huang, Kang-Hyun Jo, Juan Carlos Figueroa-García
Pages115-121
Number of pages7
Volume10362
ISBN (Electronic)9783319633121
DOIs
Publication statusPublished - 20 Jul 2017
Event13th International Conference Intelligent Computing Theories and Application - Liverpool, United Kingdom
Duration: 7 Aug 201710 Aug 2017

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743

Conference

Conference13th International Conference Intelligent Computing Theories and Application
Abbreviated titleICIC 2017
CountryUnited Kingdom
CityLiverpool
Period7/08/1710/08/17

Fingerprint Dive into the research topics of 'A performance evaluation of systematic analysis for combining multi-class models for sickle cell disorder data sets'. Together they form a unique fingerprint.

  • Cite this

    Khalaf, M., Hussain, A. J., Al-Jumeily, D., Keight, R., Keenan, R., Al Kafri, A. S., Chalmers, C., Fergus, P., & Idowu, I. O. (2017). A performance evaluation of systematic analysis for combining multi-class models for sickle cell disorder data sets. In D-S. Huang, K-H. Jo, & J. C. Figueroa-García (Eds.), Intelligent Computing Theories and Application (Vol. 10362, pp. 115-121). (Lecture Notes in Computer Science). https://doi.org/10.1007/978-3-319-63312-1_10