Big Data analytics using Artificial Intelligence techniques in medical PHM

Abstract

Today, With the development of information technology, the concept of smart healthcare became a trending research area. Smart healthcare uses a new generation of information technologies such as big data, cloud computing, and artificial intelligence(AI). These new techniques helps to transform the traditional medical system to be more intelligent, efficient, convenient, and personalized. Computer-aided diagnosis (CAD) has become one of the major research subjects in medical computing and clinical diagnosis. However, how to efficiently and effectively make accurate diagnosis remains a challenging problem in data-driven models. In this thesis, we are interested in improving the performance of computer-aided diagnostic systems in the medical field by increasing the quality of medical data and the analytical techniques. To this end, several contributions have been proposed. First, we proposed an extension of Prognostic and Health Management (PHM) approaches in order to exploit its potential by adapting advanced industrial diagnostic models to medical diagnostics. Secondly, we focused on improving computer-assisted diagnosis, particularly in the dermatology field, using AI techniques as well as those of Big data. The proposed methods and the results obtained were validated by an extensive comparative analysis using benchmarks and private medical data