This paper compares two classifiers: Pseudo Bayesian and Neural Network for assisting
in making diagnoses of psychiatric patients based on a simple yes/no questionnaire
which is provided at the outpatient’s first visit to the hospital. The classifiers
categorize patients into three most commonly seen ICD classes, i.e. schizophrenic,
emotional and neurotic disorders. One hundred completed questionnaires were utilized
for constructing and evaluating the classifiers. Average correct decision rates were
73.3% for the Pseudo Bayesian Classifier and 77.3% for the Neural Network classifier.
These rates were higher than the rate which an experienced psychiatrist achieved based
on the same restricted data as the classifiers utilized. These classifiers may be
effectively utilized for assisting psychiatrists in making their final diagnoses.
Keywords:
Bayesian Classification - Neural Network - ICD-10 - Psychiatric Diagnosis - Pattern
Recognition
