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Classification of Illness

Classification of Illness

The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe and...

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Updated: May 8, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Feature selection method based on artificial bee colony algorithm and support vector machines for medical datasets

Mustafa Serter Uzer¹, Nihat Yilmaz, Onur Inan

¹Electrical-Electronics Engineering, Faculty of Engineering, Selcuk University, Konya, Turkey. msuzer@selcuk.edu.tr

Thescientificworldjournal

|August 29, 2013

Summary

This summary is machine-generated.

This study enhances disease diagnosis by using the artificial bee colony (ABC) algorithm for feature selection and support vector machines (SVM) for classification, achieving high accuracy in identifying liver diseases and diabetes.

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Area of Science:

Computational biology
Medical informatics
Machine learning

Background:

Liver diseases and diabetes are prevalent conditions significantly impacting quality of life.
Accurate and efficient diagnostic methods are crucial for timely intervention and treatment.
Feature selection plays a vital role in improving the performance of classification models in medical datasets.

Purpose of the Study:

To evaluate the impact of eliminating irrelevant features on classification accuracy for liver disease and diabetes datasets.
To develop and validate a hybrid approach combining the artificial bee colony (ABC) algorithm and support vector machines (SVM) for disease diagnosis.
To assess the effectiveness of the proposed method in comparison to existing classification techniques.

Main Methods:

A hybrid approach integrating the artificial bee colony (ABC) algorithm for optimal feature selection.
Support vector machines (SVM) classifier employed for disease classification based on selected features.
Utilized hepatitis, liver disorders, and diabetes datasets from the UCI Machine Learning Repository.
Implemented 10-fold cross-validation to ensure robust performance evaluation.

Main Results:

The proposed hybrid system achieved high classification accuracies: 94.92% for hepatitis, 74.81% for liver disorders, and 79.29% for diabetes.
Feature elimination using the ABC algorithm significantly improved classification performance.
The method demonstrated superior results compared to other reported accuracies for these datasets.

Conclusions:

The hybrid ABC-SVM approach is highly effective for feature selection and classification in medical datasets.
This method shows significant promise for improving the accuracy and efficiency of diagnosing liver diseases and diabetes.
The findings suggest broad applicability of this approach in various pattern recognition tasks within medical informatics.