Expert Systems With Applications 95 (2018) 261–271
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Expert Systems With Applications journal homepage: www.elsevier.com/locate/eswa
A fuzzy inference- fuzzy analytic hierarchy process-based clinical decision support system for diagnosis of heart diseases Somayeh Nazari a, Mohammad Fallah b, Hamed Kazemipoor a,∗, Amir Salehipour c a
Industrial Engineering, Islamic Azad University, Parand Branch, Iran Industrial Engineering, Islamic Azad University, Central Tehran Branch, Iran c School of Mathematical and Physical Sciences, University of Technology Sydney, Australia b
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Article history: Received 6 July 2017 Revised 31 October 2017 Accepted 1 November 2017 Available online 1 November 2017 Keywords: Expert systems Fuzzy rules Heart disease diagnosis Heart disease prediction
a b s t r a c t Many organizations and institutions are implementing accurate and practical tools to accelerate decisionmaking process. In this regard, hospitals and healthcare centers are not exceptions, in particular, because they directly impact the health and well-being of the community. When it comes to disease diagnosis, practitioners may have different opinions, which lead to different decisions and actions. On the other hand, the amount of available information, even in a case of a typical disease is so vast that rapid and accurate decision-making may be difficult. For example, practitioners may prescribe several expensive tests in order to diagnose a heart disease whereas many of those tests might not even be required. Accordingly, a Clinical Decision Support System (CDSS) can be very helpful here. In particular, such a CDSS can be developed as an expert system for those patients who have a high likelihood of developing heart diseases. This study develops an expert system based on Fuzzy Analytic Hierarchy Process (AHP) and Fuzzy Inference System in order to evaluate the condition of patients who are being examined for heart diseases. The Fuzzy AHP is used to calculate weights for different criteria that impact developing heart diseases, and the Fuzzy Inference System is used to assess and evaluate the likelihood of developing heart diseases in a patient. The developed system has been implemented in a hospital in Tehran. The outcomes show efficiency and accuracy of the developed approach. © 2017 Elsevier Ltd. All rights reserved.
1. Introduction
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Hospitals and medical centres are implementing accurate, efficient and practical tools to accelerate decision-making process. These tools heavily rely on optimization, computation, and forecasting models and techniques (see for example, Salehipour and Sepehri (2012), Rais and Viana (2011), and Fitzgerald and Dadich (2009)). In addition to this, increased computational power along with data storage have resulted in the usage of artificial intelligence-based systems in healthcare. In fact, increasing growth of medical knowledge and information can be considered as an opportunity for computer systems (Chakraborty, Chakraborty, & Mukherjee, 2016). A broad variety of smart systems have been developed to improve community health, reduce healthcare costs and promote quality of life, among others. Such systems heavily rely on artificial intelligence. Potential applications of artificial intelligence in healthcare may be summarized as the followings (Adeli & Neshat, 2010).
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Corresponding author. E-mail address:
[email protected] (H. Kazemipoor).
https://doi.org/10.1016/j.eswa.2017.11.001 0957-4174/© 2017 Elsevier Ltd. All rights reserved.
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Evaluating, organizing, and documenting medical and healthcare information Providing tools to support decision-making, medical research and education Preparing databases for future practice
This study utilizes expert systems and fuzzy logic, and develops an intelligent system, which is capable of diagnosing occurrence of heart diseases. Traditionally, practitioners and specialists rely on their knowledge and experience when examining patients. This causes variation across practitioners, and therefore, minimizing this variation, which may be attained via seeking a second opinion, consulting with a team of practitioners, and/or using a Clinical Decision Support System (CDSS), would have lifesaving consequences, not to mention the associate savings in the healthcare system. A Clinical Decision Support Systems (CDSS) is computer software designed to contribute to clinical treatments and diagnoses (Berner, 2007). Such a system applies clinical knowledge and information to diagnose various diseases and to describe medical recommendations for patients. This system is not designed as substituent for practitioners but to help medical experts to diagnose
