ArticleDiagnostics (Basel, Switzerland)2019
Framework for the Development of Data-Driven Mamdani-Type Fuzzy Clinical Decision Support Systems.
Article in Diagnostics (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Performance of expert fuzzy systems for prediction of rabbit feed intake after weaning.Tropical animal health and production · 2024Article
- Diagnosis of heart diseases: A fuzzy-logic-based approach.PloS one · 2024Article
- Intelligent Fuzzy System to Predict the Wisconsin Breast Cancer Dataset.International journal of environmental research and public health · 2023Article
- Intelligent System for Detecting Deterioration of Life Satisfaction as Tool for Remote Mental-Health Monitoring.Sensors (Basel, Switzerland) · 2022Article
- Predictive Sequential Research Design to Study Complex Social Phenomena.Entropy (Basel, Switzerland) · 2021Article
- Developing a clinical decision support system based on the fuzzy logic and decision tree to predict colorectal cancer.Medical journal of the Islamic Republic of Iran · 2021Article
- Applications of Computational Methods in Biomedical Breast Cancer Imaging Diagnostics: A Review.Journal of imaging · 2020Review
- A five-year (2015 to 2019) analysis of studies focused on breast cancer prediction using machine learning: A systematic review and bibliometric analysis.Journal of public health research · 2020Article
- Decision Trees for Predicting the Physiological Responses of Rabbits.Animals : an open access journal from MDPI · 2019Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Clinical decision support systems (CDSS) have been designed, implemented, and validated to help clinicians and practitioners for decision-making about diagnosing some diseases. Within the CDSSs, we can find Fuzzy inference systems. For the reasons above, the objective of this study was to design, to implement, and to validate a methodology for developing data-driven Mamdani-type fuzzy clinical decision support systems using clusters and pivot tables. For validating the proposed methodology, we applied our algorithms on five public datasets including Wisconsin, Coimbra breast cancer, wart treatment (Immunotherapy and cryotherapy), and caesarian section, and compared them with other related works (Literature). The results show that the Kappa Statistics and accuracies were close to 1.0% and 100%, respectively for each output variable, which shows better accuracy than some literature results. The proposed framework could be considered as a deep learning technique because it is composed of various processing layers to learn representations of data with multiple levels of abstraction.
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