A Medical Diagnostic System for Chronic Kidney Disease by Using Fuzzy Logic
Chronic kidney disease is a severe and deadly condition and should be identified at its initial stage. Otherwise, the life of a patient is in critical condition, or the patient can die if it grows at its advanced stage. Hence, the detection of this deadly disease is essential to elevate the life of a patient having chronic kidney disease (CKD). Although the active control and primary diagnosis can assist in the avoidance of its further growth, but this treatment methodology can be applied only after the detection of the disease. A fuzzy expert system is evolved in this paper for the diagnosis of chronic kidney disease. The information from the experts on this disease, database and clinical practice guidelines are acquired in order to develop the model. The fuzzy logic is utilized in the development of the proposed system as it is a powerful approach of soft computing and able to even deal with fuzzy values. The system takes eight inputs which are the clinical presentation and tests of the disease, and as a result, it obtained one output which depicts the current stage of the CKD in a particular patient. The entire implementation of the designed system has been done by using effective software named MATLAB. Additionally, the developed system is also tested and validated. The performance parameters of the system are evaluated as 95.74 percent sensitivity, 95.74 percent precision, 96.22 percent specificity and 96 percent accuracy in classification. These figures of the performance parameters make this system accurate, effective and efficient. Thus, the designed fuzzy expert system is a medical diagnostic system for CKD which can be used as a supporting tool for the specialists and can able to help the experts in making a correct decision according to the provided inputs.
Authors
- Janpreet Singh (ORCID: https://orcid.org/0000-0002-5787-2518)
- Dalwinder Singh (ORCID: https://orcid.org/0000-0002-4969-0167)
Institutions
- Lovely Professional University (IN)
Publication Details
- Published
- 2024-11-13
- DOI
- https://doi.org/10.1109/ictacs62700.2024.10840875
- Primary Topic
- Artificial Intelligence in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 0.00