Homogenous Ensembles of Data Mining Algorithms in Predicting Liver Disease
Journal Title: Annals. Computer Science Series - Year 2018, Vol 16, Issue 2
Abstract
Application of data mining algorithms to medical fields have been of interest as it helps patients get access to a better and faster healthcare. In this study, the effect of homogenous ensemble methods of bagging and boosting has been investigated as related to the prediction of the presence or absence of liver diseases. Experimental results show that while bagging and boosting did not improve the accuracy and sensitivity of algorithms in predicting liver disease, Boosting increased the specificity of algorithms.
Authors and Affiliations
Oladeji Samuel OMOKANYE, Taye Oladele ARO
Keywords
Related Articles
- EP ID EP540212
- DOI -
- Views 101
- Downloads 0
Quadratic Regression and Factorial Analysis on the Effect of Climatic Elements on Global Food Production and Land Nutrients in Africa
The United Nation has its number one Sustainable Development Goal (SDG #1) of No Poverty Global World which is, Agriculture and Food Security. The question to be asked is how do we make production of food like maize, ric...
How many interchanges does the selection sort make for iid geometric(p) input?
The note derives an expression for the number of interchanges made by selection sort when the sorting elements are iid variates from geometric distribution. Empirical results reveal we can work with a simpler model compa...
Neuro-Fuzzy Expert System For Diagnosis Of Thyroid Diseases
The computerization of medical procedures has been identified to be one of the major challenges in the medical sector. Several techniques have been used in order to automate the processes in diagnosis of diseases; such p...
The certification of the EHR systems according to EuroRec Seal
As in any field, in the e-health domain it is very important that the designing of a computer system should start by establishing a complete set of requirements for the architecture of the future EHR system. The present...
Analytic Approach To Face Emotion Recognition With SVM Kernels
Face emotion recognition is one of the challenges known with emotion recognition and it has received much attention during the recent years due to its application in different fields. SVM kernels were adopted to increase...