We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
Analysis of the S-ANFIS Algorithm for the Detection of Blood Infections Using Hybrid Computing.
- Authors
Khatter, Harsh; Gupta, Amit Kumar; Garg, Ruchi Rani; Sain, Mangal
- Abstract
Environment and climate change have caused a rise in a wide range of diseases and infections. In countries where overpopulation is a problem, many infections spread severely. The main focus of this paper is the detection and identification of blood diseases. An automated system that examines all potential diseases using patient information and data is needed to deal with unpredictable circumstances. Having an automated and intelligent system that evaluates the reports and counsels doctors in any other area or nation is a demand of the time. The same solutions can be identified by the proposed system. To apply the adaptive neuro-fuzzy inference system (ANFIS) and related techniques to predict chronic diseases early, the authors have gone through various existing models and case studies on diabetics and other patients. The proposed approach, called S-ANFIS which is using the hybrid approach, is based on ANFIS and includes content curation and intelligence analysis in addition to comparison with current models. As a result, the suggested model outperforms other approaches in terms of disease prediction accuracy, with a score of 88.6%.
- Subjects
BLOOD diseases; ALGORITHMS; CHRONIC diseases; INFECTION; CLIMATE change
- Publication
Electronics (2079-9292), 2022, Vol 11, Issue 22, p3733
- ISSN
2079-9292
- Publication type
Article
- DOI
10.3390/electronics11223733