Computer aided diagnostic systems are used to examine the kidney features for the early identification of kidney diseases. The optimal path is detected during the segmentation of magnetic resonance images of kidneys for diagnosis of Chronic Kidney Disease (CKD). The registration is performed on the selected portion of an image to increase the accuracy. The image-processing techniques are applied on magnetic resonance images of total kidney volume. The detection of kidney disease and disorder can be done by studying the features extracted from an acquired image. It is tough for individual to recover from every kidney disease.

Nowadays, due to increase in population and unhealthy lifestyle, number of patients with renal disorder is increasing. This disease can also lead to the damage of other neighbor organs of a kidney. The kidney is the natural filter of blood and it discharges the wastage from the body in the form of urine. It basically occurs when the kidney stops its functionality like the maintenance of pH value, water, and salt in the blood. IntroductionĬhronic kidney disease or chronic renal disorders arise due to the lack of functionality of nephrons in the kidney. The proposed FES is implemented in the MATLAB. 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. This system supports the doctors in assessment of chronic kidney disease among patients. It is observed that this system succeeds in 93.75% of the tests. Totally 80 tests were performed on the FES developed in this research work, and the generated output was compared with expected output. The output of the system shows the current stage of patient’s kidney disease. 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 normality tests are applied on different input parameters. 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 proposed fuzzy rule-based expert system is developed with the help of clinical practice guidelines, database, and the knowledge of a team of specialists. In this research paper, design and development of a fuzzy expert system (FES) to identify the current stage of chronic kidney disease is proposed. To increase the life span of a patient, it is necessary to detect such diseases in early stages. Primary diagnosis and active control avoid its progression. Chronic kidney disease is a life-threatening complication.

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