An Inconsistency-Based Hybrid Feature Selection Approach for Enhancing Medical Classification Modeling

Rong Zhao, Ghassan Saleh ALDharhani*, Kuru Ratnavelu, Sathiyaraj Thambiayya, Muhammed Basheer Jasser, Anwar P.P.Abdul Majeed, Yang Luo

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

Abstract

Artificial Intelligence is an essential tool for early disease recognition and supporting patient condition monitoring in the future. Timely and exact conclusions about the type of disease are significant for treatment and life extension. A combination of classification and feature selection algorithms can effectively handle complex datasets in the medical field and improve the accuracy of disease diagnosis and treatment. In this paper, we propose a new hybrid feature selection algorithm that uses inconsistency metrics as a filter method for the first step, and then feeds the resulting dataset into a wrapper method, which ultimately results in a reduced dataset. The proposed HFSIM algorithm is tested on five datasets in the medical domain from Kaggle. The obtained feature subsets are verified on three classification algorithms, KNN, LR and RF of machine learning to validate the effectiveness of the proposed algorithm. The experimental results show that the proposed algorithm obtains feature subsets with low dimensionality on most of the datasets and also has high classification accuracy after testing on the classifiers.

Original languageEnglish
Title of host publicationSelected Proceedings from the 2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024 - Advances in Intelligent Manufacturing and Robotics
EditorsWei Chen, Andrew Huey Ping Tan, Yang Luo, Long Huang, Yuyi Zhu, Anwar PP Abdul Majeed, Fan Zhang, Yuyao Yan, Chenguang Liu
PublisherSpringer Science and Business Media Deutschland GmbH
Pages662-671
Number of pages10
ISBN (Print)9789819639489
DOIs
Publication statusPublished - 2025
Event2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024 - Suzhou, China
Duration: 22 Aug 202423 Aug 2024

Publication series

NameLecture Notes in Networks and Systems
Volume1316 LNNS
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

Conference2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024
Country/TerritoryChina
CitySuzhou
Period22/08/2423/08/24

Keywords

  • Classification
  • Hybrid Feature Selection
  • Inconsistency Measure
  • Machine Learning
  • Medical Datasets

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