In the present-day scenarios of medical systems, the measurements and devices used for detecting a disease are rapidly evolving. There is an increase in the need for improved methods of computation associated with medical systems.

This paper describes a method called pairwise ANFIS. Pairwise ANFIS is used to determine the disorder degree of the obstructive sleep apnea syndrome (OSAS). The authors indicate that the “method has produced very promising results” in detecting this disease. The first part of the paper briefly explains the symptoms of the disease. Then, the authors proceed with the explanation of various devices and measurements used for its detection. The second half of the paper explains using pairwise ANFIS for detecting OSAS. The paper concludes by presenting different levels of accuracy obtained by using the pairwise ANFIS for various stages of this disease. The authors describe the pairwise ANFIS approach as a “one against all method.”

Overall, the paper is well written and presented. It provides good explanations of the various symptoms of OSAS and a reasonable account of the current state-of-the-art devices and measurements involved in detecting it. Table 1 nicely presents statistical information of the clinical measurements used to detect OSAS.

It is extremely critical to use innovative computing methods, such as pairwise ANFIS, to accelerate the detection of OSAS. As the authors point out, it would be very useful to have an online Web-based tool that can accept clinical measurements and provide useful information on detecting this disease. Using methods based on artificial intelligence (AI), such as pairwise ANFIS, can bring about a paradigm shift in the detection of such diseases as OSAS. Although the paper’s figures are adequate, there is room for improvement.

In conclusion, I like this paper and the flow of its presentation.