Autonomic Computing Based Respiratory Disorders Assessment Using Speech Parameters: A Systematic Review

Respiratory disorders (RD) are common health problems, and they represent a huge health burden to people around the world. Nowadays, speech analysis is a very important research area in the biomedical field. Therefore, speech analysis can be a useful tool for pre-diagnosing of RD. This review paper aimed to select the features of speech affected by RD and to define autonomic computing in terms of self-care and self-management in disease assessment using the machine learning (ML) paradigm. Selected articles in this work have been published in the last decade and are written in the English language.722 articles in total were obtained, of which 15 were chosen based on the meta-analyses (PRISMA) approach, and for systematic literature reviews (SLR), the reporting items were preferred. Articles were compiled by searching different databases such as Google Scholar, Crossref, and PubMed. This SLR mainly contributed to identifying the role of autonomic computing in the application of self-management for the assessment of RD in ML classifiers. It was observed that most of the research work on respiratory disorders with self-management applying speech analysis was done during the year 2012. Then gradually less work was done in this area until 2018 and in 2019, it increased again and stayed finally stable until 2022. It was concluded that the important information provided by this SLR is efficient model to detect and characterize RDs by applying speech parameters using autonomic computing.