Improved Multiple Disease Detection Using Modified ANFIS and K-Medoid Clustering

The present study focuses on the use of ML and DL methods to forecast the occurrence of common disorders in the area, including diabetes, allergies. Diabetic retinopathy (ADKD) and diabetic nephropathy (NDKD) are two major consequences of diabetes that researchers are also trying to quantify in thei...

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Bibliographic Details
Published in2025 Global Conference in Emerging Technology (GINOTECH) pp. 1 - 6
Main Authors Chandanan, Amit Kumar, Kandepu, Sunitha, Giri, Neeraj, Somasundaram, B., Ravi, C. N., Lathigara, Amit
Format Conference Proceeding
LanguageEnglish
Published IEEE 09.05.2025
Subjects
Accuracy
ADKD
ANFIS
Biomedical imaging
CFCM
CFI
Deep learning
Global Positioning System
Insulin
IVP-SVM
LSE
M-ANFIS
Medical devices
Medical services
Metabolism
NDKD
Performance evaluation
Systematic literature review
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Summary:The present study focuses on the use of ML and DL methods to forecast the occurrence of common disorders in the area, including diabetes, allergies. Diabetic retinopathy (ADKD) and diabetic nephropathy (NDKD) are two major consequences of diabetes that researchers are also trying to quantify in their study subjects. The previously processed information is queried for variables like illness name, indicators, medicine name, country, year, sex, and so on. Then, the Enclosed Frequent Item-set (CFI) count is determined. The CFI count's diversity is subsequently calculated. The disorders that are common in society can be recognized, at last, by using the M-ANFIS method. Clustered the CFI volatility of medical data is done using the M-ANFIS (Modified Adaptive Neuro-Fuzzy Inference System) technique using the k-Medoid clustered technique. The degree of accuracy achieved by the suggested strategy is 96.342%.
DOI:10.1109/GINOTECH63460.2025.11076895