Service Mechanism for Diagnosis of Respiratory Disorder Severity Using Fuzzy Logic for Clinical Decision Support System
One of the most chronic lung diseases known worldwide is respiratory disorder. Respiratory disorder is based on the functional consequences of airway inflammation, calamitous nature, and improper diagnosis. In this paper our aim is to develop a service di
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Abstract One of the most chronic lung diseases known worldwide is respiratory disorder. Respiratory disorder is based on the functional consequences of airway inflammation, calamitous nature, and improper diagnosis. In this paper our aim is to develop a service discovery mechanism for diagnosis of respiratory disorder severity using fuzzy logic for a clinical decision-support system. A mechanism system has been created for a fuzzy rule-based system. Five symptoms have been taken for the decision of the respiratory disorder conditions. Keywords Respiratory disorder
Information system Fuzzy logic
1 Introduction Respiratory disorder is a major issue of discussion worldwide [1, 2]. According to a recent survey the United States alone has 7.2 million teenagers and 14.8 million stricken adults in total affecting an estimated 350 million families [WHO], with casualties of approximately 1 out of every 250 deaths [3, 4]. The major causes for such a boost are still not apparent and identified although it may imitate augmented exposure to environmental risk factors [5]. Some sources claim respiratory disorder is underdiagnosed in teenagers, with events of coughing and sneezing or diagnosis of respiratory disorder earlier can show a basic feature of analysis [6]. Because doctors have different opinions there is a strong possibility of variability of information [7–9]. In order to quantify this information we have used fuzzy set theory developed by Zadeh [10] in order to derive a crisp solution. We use a fuzzy rule base that will refine the output diagnostic process [11].
Faiyaz Ahamad (&) Manuj Darbari Department of CSE, BBD University, Lucknow, India e-mail: [email protected] Rishi Asthana Department of Electrical & Electronics, IMS Engineering College, Ghaziabad, India © Springer Science+Business Media Singapore 2016 N.R. Shetty et al. (eds.), Emerging Research in Computing, Information, Communication and Applications, DOI 10.1007/978-981-10-0287-8_29
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2 Designing of Fuzzy Inference System for Diagnosis of Respiratory System The goal of this paper is to establish a method that could possibly use the concept of a fuzzy inference system (FIS) for respiratory disorder severity. Our work is divided into two sections, the first phase deals with creation of data for the analysis. The second phase deals with generation of a FIS that can predict the exact result. The process flow diagram shown in Fig. 1 represents comprehensive software architecture in order to diagnose respiratory disorder. In order to the judge the complexity of severity we have combined the modules, for example, compliance and decision-support systems that maintain a high degree of cohesion and low coupling [12]. The above system gives the entire blueprint of the information flow
Fig. 1 Comprehensive software architecture of fuzzy inference system for diagnosis of respiratory system information system
Service Mechanism for Diagnosis of Respiratory Disorder …
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and the activities being performed. The architecture
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