DESIGN OF A FUZZY MODEL FOR THALASSEMIA DISEASE DIAGNOSIS: USING MAMDANI TYPE FUZZY INFERENCE SYSTEM (FIS)
Objective: Diagnosis process of Thalassemia requires several types of medical test, and results of this test together identify the stage of Thalassemia. The objective of this study is to design a Fuzzy Inference System to diagnose the severity of the Thalassemia disease of a patient by using Fuzzy Logic.
Methods: In this paper, a new approach based on fuzzy inference system was presented for prediction of Thalassemia disease in patients. The proposed Fuzzy model combined the expertâ€™s knowledge and the fuzzy logic approach which is then combined in fuzzy rule base to diagnose the presence of the disease. The performances of the system graphically represented by fuzzy inference system tools in MATLAB8.4.
Results: It was found that our program matched the doctorâ€™s diagnosis in 12 cases perfectly. The other 3 were marginally off. This results with an accuracy of about 80 %.
Conclusion: The result suggests that the model provides the most effective way to identify Thalassemia type in patients. The results in this work can be obtained by a simple and inexpensive method. This would generate, in economic terms, significant savings.
Keywords: CBC Test, Fuzzy Logic, Mamdani Fuzzy Inference System, Thalassemia Disease
2. Yousafzai YM, Khan S, Raziq F. Beta thalassaemia trait: hematological parameters. J Ayub Med Coll Abbottabad 2010;22:84-6.
3. Dell'Edera D, Pacella E, Epifania AA, Benedetto M, Tinelli A, et al. Importance of molecular biology in the characterization of Î²-thalassemia carriers. Eur Rev Med Pharmacol Sci 2011;15:79-86.
4. Edera DD, Benedetto M, Leo M, Santacesaria C, Arianna Allegretti A, Lupo MG, et al. Identification of patients with defects in the globin genes by analyzing blood parameters and genetic study: report of five cases. J Hematol Malignancies 2013;3:29-36.
5. Zadeh LA, Fuzzy Sets. This weekâ€™s citation classic. Information Control 1965;8:338-53.
6. Zadeh LA. Outline of a new approach to the analysis of complex systems and decision processes. IEEE Trans Syst Man Cybern 1973;3:28-44.
7. Mahfouf M, Abbod MF, Linkens DA. A survey of fuzzy logic monitoring and control utilization in medicine. Artificial Intelligence Med 2001;21:27-42.
8. Aramideh J, Jelodar H. Application of fuzzy logic for the presentation of an fuzzy expert system to diagnose Anemia. Indian J Sci Technol 2014;7:933-8.
9. Hashmia A, Khan MS. Diagnosis blood test for liver disease using fuzzy logic. Int J Sci: Basic Appl Res 2015;20:151-83.
10. Adeli A, Neshat M. A fuzzy expert system for heart disease diagnosis. proceedings of the international multi-conference of engineers and computer scientists 2010;1:134-9.
11. Lavanya K, Saleem DMA, Sriman NA. Fuzzy rule-based inference system for detection and diagnosis of lung cancer. Int J Latest Trends Computing 2011;165:2045-5364.
12. Grow K, Vashist M, Abrol P, Sharma S, Yadav R. Beta thalassemia in India: current status and the challenges ahead. Int J Pharm Pharm Sci 2014;6:28-33.
13. Anunchai A. Chalorthamh N, Ruangrajitpakornt T, Limwongseh C, Supnithit T, Tongsimai S. A development of knowledge representation for thalassemia prevention and control program.
Natural Language Processing and Knowledge Engineering (NLP-KE), 2011. 7th International Conference IEEE; 2011. p. 190-3.
14. Galanello R, Melis MA, Ruggeri R, Addis M, Scalas MT, Maccioni L, et al. Beta Â°thalassemia trait in Sardinia. Hemoglobin 1979;3:33â€“46.