Feature Selection Using Intelligent Agents for Time Improvement in Medical Diagnosis Systems

Feature selection is an important task in medical applications, given that the dimensionality and numerosity of such datasets are very high. In these cases, the time parameter also becomes important, along with classification accuracy, in estimating the performance of a learning model. This approach proposes intelligent agent teams that are capable of automatically discovering the best time to build models while keeping the general accuracy at the highest levels. For computing attributes’ relevance for the classification process, several techniques were used: Wrapper Evaluation, Information Gain, gain ratio, correlation, and Relief Attribute Evaluator. One of our contributions is the Threshold Agent, which evaluates the attributes as class attributes and considers the relevance of the attributes returned by the Wrapper method. This agent selects the strongest attributes (above a threshold value) and returns a subset that is learnt by the next attribute evaluation method within the Feature Selection Agent. The proposed agents discovered that an optimum subset composed of 20 attributes (out of 133 attributes of the initial dataset) leads to accuracy rates equal to the ones registered on the entire dataset, meaning 98%, using the Naive Bayes learning model, while improving the time taken to build the model from 0.1 s to 0.03 s. For the proposed dataset, Naïve Bayes outperformed other classification techniques, such as J48, Random Forest, and Dl4MlpClassifier. The proposed agents also integrated the best discovered model into a chatbot that performs medical diagnoses based on the symptoms collected from users.