User-requirements driven learning.

This paper describes an approach for deriving classification knowledge from databases, taking into account user preferences. These preferences especially concern the trade-off between different kinds of costs and performance indicators of the classification scheme to be developed. We analyze what knowledge, provided by the user, can be used at various stages of the machine learning process to influences the development of the classifier. We restrict ourselves in this paper mainly to the generation of classification trees.

Evaluation of new self-learning techniques for the generation of criteria for differentiation of wide-QRS tachycardia in supraventricular tachycardia and ventricular tachycardia.

This study presents a comparison of three different methods for differentiating between supraventricular and ventricular tachycardias with wide-QRS complex. One set of criteria, derived using classical statistical techniques, was compared with two new self-learning computer techniques: the artificial neural networks and the induction algorithm approach. By analyzing the results obtained in an independent test set, using these new techniques, the criteria defined by the classical method could be improved.

Development of ECG criteria to diagnose the number of narrowed coronary arteries in rest angina using new self-learning techniques.

Recently, an evaluation of the value of the resulting electrocardiogram recorded during chest pain for identifying high-risk patients with three-vessel or left main stem coronary artery disease has resulted in the definition of one characteristic pattern: ST-segment depression in leads I, II, and V4-V6 and elevation in lead aVR. This study evaluated the generation of such criteria using two self-learning techniques: neural networks and induction algorithms. In 113 patients, five variables, including the amount of ST elevation, the number of leads with abnormal ST-segments, and this above-mentioned characteristic sign, were correlated with the number of narrowed vessels. All patients were randomly subdivided into a training (n = 63) and test set (n = 50), stratified for both this characteristic sign and for the vessel involved. Using the learning set, the neural network and the induction algorithm were trained separately to identify (1) pure left main stem disease and (2) three-vessel disease and left main stem disease. The neural network was trained for 1,000 runs. The induction algorithm was trained, allowing all variables to be used in any order. The experiments were repeated after adding weight factors to promote the recognition of the more severe cases. Subsequently, the ST elevation in all 12 leads was added to the training and test sets, once with and once without the polarity of the ST deviation. Altogether, 18 different combinations were evaluated. Basically, the neural network and the induction algorithm approach misclassified the same cases in corresponding test combinations.(ABSTRACT TRUNCATED AT 250 WORDS)