A comparative study of classification techniques in data mining algorithms used for medical diagnosis based on DSS

Ahmed Shihab Ahmed, Hussein Ali Salah

Abstract


A significant amount of data is gathered by the healthcare sector, but it is not appropriately mined and utilized. Finding these hidden links and patterns is frequently underutilized. Our study focuses on this element of medical diagnostics by identifying patterns in the information gathered about kidney illness, liver disease, and chronic pancreatitis (CP) and designing adaptive medical decision support systems (MDSS) to assist doctors. This research compares a variety of data mining (DM) techniques, knowledge extraction tools, and software platforms for usage in a DSS for analysis using the Waikato environment for knowledge analysis (WEKA) mining tool (decision tree (DT)). The objective is to determine the most significant risk factors based on the extraction of the categorization criteria. The datasets used for this work are illustrates how successfully DM and DSS are integrated. In this research, we suggest using the C4.5 DT algorithm, Naïve Bayes (NB) algorithm, and the logistic regression (LR) algorithm to categorize these diseases and evaluate their performance and accuracy rates. It inferred that the C4.5 algorithm accuracy is 0.873% which is better than the other two algorithms in terms of rule generation and accuracy.


Keywords


C4.5 decision tree algorithm; Classification rules; Clinical DSS; Logistic regression algorithm; Naïve Bayes algorithm

Full Text:

PDF


DOI: https://doi.org/10.11591/eei.v12i5.4804

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Bulletin of EEI Stats

Bulletin of Electrical Engineering and Informatics (BEEI)
ISSN: 2089-3191, e-ISSN: 2302-9285
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).