Decision tree induction in the diagnosis of otoneurological diseases

Expert systems have been applied in medicine as diagnostic aids and education tools. The construction of a knowledge base for an expert system may be a difficult task; to automate this task several machine learning methods have been developed. These methods can be also used in the refinement of know...

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Bibliographic Details
Published in:Medical informatics and the internet in medicine 1999, Vol.24 (4), p.277-289
Main Author: Viikki, E. Kentala, M. Juhola, I. Pyykko, K.
Format: Article
Language:English
Subjects:
Algorithms
Biological and medical sciences
Computerized, statistical medical data processing and models in biomedicine
Decision Trees
Decision Trees
Expert Systems
Otoneurology
Vertigo
Diagnosis, Computer-Assisted - methods
Diagnosis, Differential
Ear, auditive nerve, cochleovestibular tract, facial nerve: diseases, semeiology
Expert Systems
Hearing Loss, Sudden - complications
Hearing Loss, Sudden - diagnosis
Humans
Medical sciences
Meniere Disease - complications
Meniere Disease - diagnosis
Models and simulation
Neurilemmoma - complications
Neurilemmoma - diagnosis
Neuroma, Acoustic - complications
Neuroma, Acoustic - diagnosis
Non tumoral diseases
Otorhinolaryngology. Stomatology
Predictive Value of Tests
Vertigo - etiology
Vestibular Neuronitis - complications
Vestibular Neuronitis - diagnosis
Vestibule, Labyrinth - injuries
Vestibulocochlear Nerve Diseases - complications
Vestibulocochlear Nerve Diseases - diagnosis
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Summary:Expert systems have been applied in medicine as diagnostic aids and education tools. The construction of a knowledge base for an expert system may be a difficult task; to automate this task several machine learning methods have been developed. These methods can be also used in the refinement of knowledge bases for removing inconsistencies and redundancies, and for simplifying decision rules. In this study, decision tree induction was employed to acquire diagnostic knowledge for otoneurological diseases and to extract relevant parameters from the database of an otoneurological expert system ONE. The records of patients with benign positional vertigo, Meniere's disease, sudden deafness, traumatic vertigo, vestibular neuritis and vestibular schwannoma were retrieved from the database of ONE, and for each disease, decision trees were constructed. The study shows that decision tree induction is a useful technique for acquiring diagnostic knowledge for otoneurological diseases and for extracting relevant parameters from a large set of parameters.
ISSN:1463-9238
1464-5238
DOI:10.1080/146392399298302