Grammar-based automatic programming for medical data classification: an experimental study

In a computational medical model, diagnosis is the classification of disease status in the terms of abnormal or positive , normal or negative or intermediate stages . Different Machine learning techniques such as artificial neural networks (ANNs) are extensively and successfully used in disease diag...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:The Artificial intelligence review Jg. 54; H. 6; S. 4097 - 4135
Hauptverfasser: Si, Tapas, Miranda, Péricles, Galdino, João Victor, Nascimento, André
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Dordrecht Springer Netherlands 01.08.2021
Springer
Springer Nature B.V
Schlagworte:
Algorithms
Artificial Intelligence
Artificial neural networks
Automatic
Classification
Classifiers
Computer Science
Data
Decision analysis
Decision-making
Diagnosis
Discriminant analysis
Disease
Experiments
Extraction
False positive results
Feature extraction
Geometric accuracy
Grammar
Grammar, Comparative and general
Machine learning
Medical advice systems
Medical diagnosis
Medical model
Multiple criterion
Neural networks
Performance evaluation
Grammatical swarm
Automatic programming
Medical data classification
Swarm programming
ISSN:0269-2821, 1573-7462
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:In a computational medical model, diagnosis is the classification of disease status in the terms of abnormal or positive , normal or negative or intermediate stages . Different Machine learning techniques such as artificial neural networks (ANNs) are extensively and successfully used in disease diagnosis. However, there is no single classifier that can solve all classification problems. Selecting an optimal classifier for a problem is difficult, and it has become a relevant subject in the area. This paper focuses on grammar-based automatic programming (GAP) to build optimized discriminant functions for medical data classification in any arbitrary language. These techniques have an implicit power of automatic feature selection and feature extraction. This work carries out an in-depth investigation of the use of different GAP algorithms in the medical data classification problem. The objective is to identify the benefits and limitations of algorithms of this nature in the current problem. Classical classifiers were also considered for comparison purposes. Fourteen medical data sets were used in the experiments, and seven performance measures such as accuracy, sensitivity, specificity, precision, geometric-mean, F-measure, and false-positive rate are used to evaluate the performance of the produced classifier. The multiple criteria decision analysis (MCDA) demonstrates that GAP approaches are able to produce suitable classifiers for a given problem, and the GS performs better than other classical classifiers in medical data classification.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0269-2821
1573-7462
DOI:10.1007/s10462-020-09949-9