Weighted fuzzy C means and enhanced adaptive neuro-fuzzy inference based chronic kidney disease classification

Abstract

Chronic nephritic sickness is another name for Chronic Kidney Disease (CKD). Numerous complications, such as elevated blood levels, anemia, weak bones, and nerve damage, constitute a problem. It is usually possible to prevent chronic uropathy from getting worse by early identification and treatment. To circumvent these problems, current research has presented the Fruit Fly Optimization Algorithm (FFOA) and effective Multi-Kernel Support Vector Machine (MKSVM) for illness classification. Finding the best features from a collection is usually done using FFOA. MKSVM categorizes medical data using chosen dataset criteria. The accuracy of the classifier will be impacted by any range of variations in data obtained for this study. MKSVM continues to yield more incorrectly classified findings. To resolve those problems, a preprocessing step based on min-max normalization is used to normalize the input CKD data values scale. Then, significant features will be selected using Improved FFOA (IFFOA). The selected features will be clustered using Weighted Fuzzy C Means clustering (WFCM) to predict the class label of the data sample and reduce the misclassification results. Finally, as normal or abnormal, CKD classification will be performed using the Enhanced Adaptive Neuro Fuzzy Inference System (EANFIS). The suggested strategy efficacy is demonstrated by findings in fields of recall, accuracy, precision, and f-measure.

Keywords

• multi-kernel support vector machine
• fruit fly optimization algorithm
• chronic kidney disease
• significant features
• weighted fuzzy c means
• adaptive neuro-fuzzy inference system