Abstract
Chronic kidney disease is a life-threatening complication. Primary diagnosis and active control avoid its progression. To increase the life span of a patient, it is necessary to detect such diseases in early stages. In this research paper, design and development of a fuzzy expert system (FES) to identify the current stage of chronic kidney disease is proposed. The proposed fuzzy rule-based expert system is developed with the help of clinical practice guidelines, database, and the knowledge of a team of specialists. It makes use of input variables like nephron functionality, blood sugar, diastolic blood pressure, systolic blood pressure, age, body mass index (BMI), and smoke. The normality tests are applied on different input parameters. The input variables, i.e., nephron functionality, blood sugar, and BMI have more impact on the chronic kidney disease as shown by the response of surface analysis. The output of the system shows the current stage of patient’s kidney disease. Totally 80 tests were performed on the FES developed in this research work, and the generated output was compared with expected output. It is observed that this system succeeds in 93.75% of the tests. This system supports the doctors in assessment of chronic kidney disease among patients. The detection of chronic kidney disease is a serious clinical problem that comprises imprecision, and the use of fuzzy inference system is suggested to overcome this issue. The proposed FES is implemented in the MATLAB.
Highlights
Chronic kidney disease or chronic renal disorders arise due to the lack of functionality of nephrons in the kidney
It is tough for individual to recover from every kidney disease [5]. e detection of kidney disease and disorder can be done by studying the features extracted from an acquired image. e image-processing techniques are applied on magnetic resonance images of total kidney volume [6]. e registration is performed on the selected portion of an image to increase the accuracy [7]. e optimal path is detected during the segmentation of magnetic resonance images of kidneys for diagnosis of Chronic Kidney Disease (CKD) [8]
Ultrasonography images are used for detection of stages of CKDs. e stages of CKD are predicted by using machine learning techniques [12,13,14,15]. e comparisons have been made between the performances of different systems developed by using various algorithms of machine learning [16]
Summary
Chronic kidney disease or chronic renal disorders arise due to the lack of functionality of nephrons in the kidney. E main objective of this work is to develop a medical expert system for diagnosis of chronic kidney disease by using fuzzy logic. E research works discussed above provide fruitful information about the applications of expert systems for medical diagnosis and especially for the diagnosis of chronic kidney disease. E primary risk factors or causes of the chronic kidney disease have been used as the input variables in the development of a proposed medical expert system. After the processing of all blocks of fuzzy logic, it generates an output In this developed medical expert system, the output is obtained according to the health of a patient who is suffering from this disease.
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