An Efficient Artificial Intelligence Based Technique in Diseases Staging and Forecasting



Department of Mathematics and Computer Science, University of Perugia, Perugia, Italy


Artificial Intelligence (AI) techniques offer powerful objective algorithms for analysis of multimodal and high-dimensional data. Recently, these techniques have become a reliable tool in the medical domain. This paper describes an efficient technique for building an application that is capable of forecasting and classifying healthcare information using machine learning as a subfield of AI methods. The algorithm predicts a label for each sample. The sample is a single set of feature data and the label is what category the sample falls into. The algorithm takes many of these samples as the training set, builds an internal model and finally predicts the labels of other samples, called the testing set. We apply this methodology to the breast cancer staging and also to forecast the myocardial infarction and examine the risk assessment using fuzzy clustering and Framingham heart study. The results show that the proposed technique obtains credible outputs that could be integrated in an application to be used in the health care field.