The Multilayer Perceptron as an Aid to the Early Diagnosis of Myocardial Infarction

The establishment of a decision aid for the early diagnosis of myocardial infarction is described.The system uses a multi-layer Perceptron structure and is trained in the usual way. It is shown that the performance of the network can exceed that of the admitting clinicians, a panel of senior physicians in a large teaching hospital and a protocol derived using conventional statistical methods over a wide range of performance measures. In particular, the network demonstrates the highly specific behaviour necessary when making the decision whether or not to administer thrombolytic therapy-a potentially life-saving decision which must be taken in the very early stages, long before confirmatory laboratory test results are available. The network is compact and has been implemented on a portable computer. In operation it responds very quickly, giving its diagnosis and recommendations (taking account of clinical opinion) in a fraction of the time taken to input the patient's symptoms

Risk-Sensitive Diagnosis and the Role of Neural Networks

Diagnostic problem solving, whether it be fault-diagnosis in an engineering system or diagnosis of a disease in human beings, is a prime example of decision making in the face of uncertainty. Frequently, many different outcomes may correspond to an identical set of measured data or symptoms. The converse may also be true, that any given diagnosis may correspond to a number of distinct sets of diagnostic data. In addition, the data themselves may be imprecise adding to the overall uncertainty in the reasoning process, making it probablistic in nature. These factors can often be the cause of poor diagnostic accuracy and in part responsible for the difficulty in developing useful and usable diagnostic support systems. Furthermore, it would be unusual for diagnostic errors to be viewed as equally acceptable. For example, a large number of false alarms may be tolerable in the dignosis of heart attack when the decision to be made is simply admit to hospital or not. The level of acceptability changes though, when the decision to be made is whether or not to administer potentially life-threatening drugs. Evidently, the risk associated with an incorrect diagnosis is crucial to making a decision about treatment...........

The Use of Artificial Neural Networks to Predict Osteoporosis

Osteoporosis arises when the bone lose sufficient mineral to allow fractures to develop after only minimal trauma. It is an extremely common condition in post-menopausal women and is becoming more common because of the increasing number of elderly women in the population. The most devastating effects of osteoporosis arise when the patient fractures either the hip or the vertebrae. These conditions are painful and disabling and are frequently the precipitating factor for an elderly person having to give up an independent existence. The cost of treating the results of osteoporosis fractures is immense. We now have accurate and widely applicable methods for measuring the bone density and thus identifying patients at risk. However, the necessary scanners are not widely available and it is not thought to be profitable to screen the entire population at risk with bone scanners.......

Dynamic Query Algorithms for Human-Computer Interaction Based on Information Gain and the Multi-Layer Perceptron

Algorithms are presented for an "intelligent" human machine interface for efficient on-line decision making and pattern recognition. The algorithms structure the data input process by dynamically asking the user the next most "informative" question based on its current state of knowledge, to reach a conclusion as quickly as possible. Using the information gain principle in attribute selection, IQA and IQA1 dynamically generate a query process without the construction of the decision tree. A further development, IQA2, generalises IQA1 by including a multi-layer Perceptron (MLP) to mitigate the effect of noise and ambiguity and to establish incremental learning. The IQA algorithms perform well on noisy and incomplete data-sets. This is demonstrated by an example from an artificial domain and two form medical diagnosis

A Prototype Neural Network Decision-Support Tool for the Early Diagnosis of Acute Myocardial Infarction

An application of the ARTMAP neural network model to the early diagnosis of acute myocardial infarction is described. Performance results are given for 10 individual ARTMAP networks and for combinations of the networks using "pooled" decision making (the so-called voting strategy). Category nodes are pruned from the trained networks in different ways so as to improve accuracy, sensitivity and specificity respectively. The differently pruned networks are employed in a novel "cascaded" variation of the voting strategy. This allows a partitioning of the test data into predictions with a high and lower certainty of being correct, providing the diagnosing clinician with an indication of the reliability of an individual prediction. Additionally, symbolic rule extraction is performed upon the networks, allowing a domain expert to verify the networks have learned autonomously a valid set of predictive rules for the domain

Neural Networks, Heart Attack and Bayesian Decisions: An Application of the Boltzmann Perceptron Network

A decision aid is proposed for the diagnosis of the most commonly occurring cause of emergency admission to hospital in the developed world-acute myocardial infarction, or heart attack. The motivation for the proposal lies in the Bayesian ( minimum risk)decision theory which is briefly reviewed. The fact that many feedforward artificial neural networks are known to estimate the conditional class probabilities required for Bayesian decision theory is explored and one candidate-the Boltzmann Perceptron Network-is selected as possessing the most desirable properties. A brief account of the theory (based upon the so-called Boltzmann machine) underlying this little known network is presented. The Boltzmann Perceptron Network is trained to diagnose the presence or absence of myocardial infarction on data gathered from a large UK teaching hospital and is found to perform as well as senior registras with specific cardiological training (diagnostic accuracy in excess of 80%). In addition, the Boltzmann Perceptron Network is found to provide greater user confidence than the multi-layer Perceptron

GR-2 Hybrid Knowledge-Based System Using General Rules

GR-2 is a hybrid knowledge-based system consisting of a Multilayer Perceptron and a rule based system for hybrid knowledge representations and reasoning. Knowledge embedded in the trained Multilayer Perceptron (MLP) is extracted in the form of general (production) rules-- a natural format of abstract knowledge representation. The rule extraction method integrates Black-box and Open-box techniques on the MLP, obtaining feature salient and statistical properties of the training pattern set. The extracted general rules are quantified and selected in a rule validation process. Multiple inference facilities such as categorical reasoning, probabilistic reasoning and exceptional reasoning are performed in GR-2. Experiments have shown that GR2 is a reliable and general model for Knowledge Engineering

A General Method for the Discovery and Use of Rules Induced by Feedforward Artificial Neural Networks

GR2 is a hybrid connectionist/knowledge-based system consisting of a Multi-layer Perceptron and a rule-based system for knowledge representation and reasoning. Knowledge embedded in a trained Multi-layer Perceptron is extracted in the form of general (production) rules--a natural format for abstract knowledge representation. The rule extraction method integrates black-box and white-box techniques on the MLP, obtaining feature salient and statistical properties of the training pattern set. This is achieved via a heuristic based on the static connections strengths from input to output. The extracted general rules are quantified and selected in a rule validation process. Multiple inference modalities such as categorical reasoning, probablistic reasoning and exceptional reasoning can be performed in GR2. In addition, quantitative indications of a rule's validity within the domain and the importance of any antecedent within a rule can be calculated. Experiments are conducted in artificial (simple logic) domains and using data from emergency medicine. The predictive performance of the underlying neural networks is seen to be maintained whilst a valid set of rules is extracted. For the medical problem, favourable comparison is drawn with the C4.5 technique, an extension of the celebrated ID3 algorithm. The methodology can be applied to any feedforward neural network via straightforward extensions to the basic ideas and avoids the need for specialised architectures found in some other methods

Autonomously Learning Neural Networks for Clinical Decision Support.

The purpose of this contribution is to motivate the use of artificial neural networks in "intelligent" clinical decision support; to examine the advantages and limitations of two important classes of artificial neural network; to highlight the potential of intelligent decision support in the early diagnosis of heart attack; and to outline results which indicate, in particular, the potential of fuzzy ARTMAP network in this acute setting. The work to be described, demonstrates that this neural network can overcome problems in knowledge acquisition and portability, which may open the way to neural-network-based "apprentices" which learn autonomously whilst providing useful decision support

Application of the Fuzzy ARTMAP Neural Network Model to Medical Pattern Classification Tasks

This paper presents research into the application of the fuzzy ARTMAP neural network model to medical pattern classification tasks. A number of domains, both diagnostic and prognostic, are considered. Each such domain highlights a particularly useful aspect of the model. The first, coronary care patient prognosis, demonstrates the ATMAP voting strategy involving "pooled" decision-making using a number of networks, each of which has learned a slightly different mapping of input features to pattern classes. The second domain, breast cancer diagnosis, demonstrates the model's symbolic rule extraction capabilities which support the validation and explanation of a network's predictions. The final domain, diagnosis of acute myocardial infarction, demonstrates a novel category pruning technique allowing the performance of a trained network to be altered so far as to favour predictions of one class over another (e.g. trading sensitivity for specificity or vice versa). It also introduces a "cascaded" variant of the voting strategy intended to allow identification of a subset of cases which the network has a very high certainty of classifying correctly