A medical decision support system for disease diagnosis under uncertainty

We present a decision support system (DSS) modeled for medical diagnosis.Fuzzy Expert System is used to implement the proposed system.Two approaches for uncertain input values are proposed.A case study on kidney stone and kidney infection diseases was conducted. This paper presents a decision support system (DSS) modeled by a fuzzy expert system (FES) for medical diagnosis to help physicians make better decisions. The proposed system collects comprehensive information about a disease from a group of experts. To this aim, a cross-sectional study is conducted by asking physicians expertise on all symptoms relevant to a disease. A fuzzy rule based system is then formed based on this information, which contains a set of significant symptoms relevant to the suspected disease. Linguistic fuzzy values are assigned to model each symptom. The input of the system is the severity level of each symptom reported by patients. The proposed FES considers two approaches to account for uncertain inputs from patients. Two case studies on kidney stone and kidney infection were conducted to demonstrate how the proposed method could be used. A group of patients were used to validate the effectiveness of the proposed expert system. The results show that the proposed fuzzy expert system is capable of diagnosing diseases with a high degree of accuracy and precision comparing to a couple of machine learning methods.

[1]  Mohammad Hossein Fazel Zarandi,et al.  Type-2 fuzzy rule-based expert system for Ankylosing spondylitis diagnosis , 2015, Annual Conference on the North American Fuzzy Information Processing Society.

[2]  Rahul Chipalkatty,et al.  Human-in-the-loop control for cooperative human-robot tasks , 2012 .

[3]  Douglas C. Montgomery,et al.  Introduction to Statistical Quality Control , 1986 .

[4]  J. Kacprzyk,et al.  The Ordered Weighted Averaging Operators: Theory and Applications , 1997 .

[5]  Farzad Firouzi Jahantigh,et al.  A computer-aided diagnostic system for kidney disease , 2017, Kidney research and clinical practice.

[6]  Rashedur M. Rahman,et al.  Diagnosis of kidney disease using fuzzy expert system , 2014, The 8th International Conference on Software, Knowledge, Information Management and Applications (SKIMA 2014).

[7]  Michelle Chen,et al.  A Model for Spheroid versus Monolayer Response of SK-N-SH Neuroblastoma Cells to Treatment with 15-Deoxy-PGJ 2 , 2016, Comput. Math. Methods Medicine.

[8]  Jiten Chandra Dutta,et al.  Early diagnosis of dengue disease using fuzzy inference system , 2016, 2016 International Conference on Microelectronics, Computing and Communications (MicroCom).

[9]  Olatunji Mumini Omisore,et al.  A web based decision support system driven by fuzzy logic for the diagnosis of typhoid fever , 2013, Expert Syst. Appl..

[10]  Marvin Bergsneider,et al.  A semi-automated workflow solution for multimodal neuroimaging: application to patients with traumatic brain injury , 2015, Brain Informatics.

[11]  Ahsan Raja Chowdhury,et al.  Human Disease Diagnosis Using a Fuzzy Expert System , 2010, ArXiv.

[12]  Mitra Mahdavi-Mazdeh,et al.  Predicting Renal Failure Progression in Chronic Kidney Disease Using Integrated Intelligent Fuzzy Expert System , 2016, Comput. Math. Methods Medicine.

[13]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[14]  Christoph S. Herrmann,et al.  A Hybrid Fuzzy-Neural Expert System for Diagnosis , 1995, IJCAI.

[15]  Omar Adwan,et al.  Automatic Heart Disease Diagnosis System Based on Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS) Approaches , 2014 .

[16]  Klaus-Peter Adlassnig,et al.  Fuzzy Set Theory in Medical Diagnosis , 1986, IEEE Transactions on Systems, Man, and Cybernetics.

[17]  Henry C. W. Lau,et al.  On A Responsive Replenishment System: A Fuzzy Logic Approach , 2006 .

[18]  Henry C. W. Lau,et al.  On A Responsive Replenishment System: A Fuzzy Logic Approach , 2003, 2006 4th IEEE International Conference on Industrial Informatics.

[19]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[20]  J. Merigó,et al.  Fuzzy Generalized Hybrid Aggregation Operators and its Application in Fuzzy Decision Making , 2010 .

[21]  Dimitrios I. Fotiadis,et al.  Automated Diagnosis of Coronary Artery Disease Based on Data Mining and Fuzzy Modeling , 2008, IEEE Transactions on Information Technology in Biomedicine.

[22]  V. Jha,et al.  Chronic kidney disease: global dimension and perspectives , 2013, The Lancet.

[23]  Andreas Holzinger,et al.  Interactive knowledge discovery with the doctor-in-the-loop: a practical example of cerebral aneurysms research , 2016, Brain Informatics.

[24]  Osman N. Uçan,et al.  Diagnosis of Renal Failure Disease Using Adaptive Neuro-Fuzzy Inference System , 2010, Journal of Medical Systems.

[25]  Kiran Jyoti,et al.  A Novel Approach for Heart Disease Diagnosis using Data Mining and Fuzzy Logic , 2012 .

[26]  Mehmet Can,et al.  Diagnosis of Parkinson’s Disease using Fuzzy C-Means Clustering and Pattern Recognition , 2013, SOCO 2013.

[27]  M. S. Ali,et al.  Fuzzy Expert Systems (FES) for Medical Diagnosis , 2013 .

[28]  Ebrahim Mamdani,et al.  Applications of fuzzy algorithms for control of a simple dynamic plant , 1974 .

[29]  Giacomo Patrizi,et al.  Operational research techniques in medical treatment and diagnosis: A review , 2000, Eur. J. Oper. Res..

[30]  Mohammad Hossein Fazel Zarandi,et al.  Fuzzy rule-based expert system for diagnosis of thyroid disease , 2015, 2015 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB).

[31]  Yuhong Xiang,et al.  Diagnosis of patients with chronic kidney disease by using two fuzzy classifiers , 2016 .

[32]  Okure Udo Obot,et al.  Clinical decision support system (DSS) in the diagnosis of malaria: A case comparison of two soft computing methodologies , 2011, Expert Syst. Appl..

[33]  Haeng-Kon Kim,et al.  A Medical Decision Support System (DSS) for Ubiquitous Healthcare Diagnosis System , 2014 .