ONCObc-ST: An Improved Clinical Reasoning Algorithm Based on Select and Test (ST) Algorithm for Diagnosing Breast Cancer

@article{Oyelade2019ONCObcSTAI,
  title={ONCObc-ST: An Improved Clinical Reasoning Algorithm Based on Select and Test (ST) Algorithm for Diagnosing Breast Cancer},
  author={Olaide Nathaniel Oyelade and Sunday A. Adewuyi},
  journal={Current Research in Bioinformatics},
  year={2019}
}
The need for an accurate reasoning algorithm is usually necessitated by the sensitivity of domain of (medicine as example) application of such algorithms. Most reasoning algorithms for medical diagnosis are either limited by their techniques or accuracy and efficiency. Even the Select and Test (ST) algorithm which is considered a more approximate reasoning algorithm is also limited by its approach of using bipartite graph in modeling domain knowledge and making inference through the use of… 
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References

SHOWING 1-10 OF 46 REFERENCES
Select and Test (ST) Algorithm for Medical Diagnostic Reasoning
TLDR
The focus of this paper is on the algorithm, which is intended to give a theoretical proof that medical expert systems are achievable; the design and implementation of a knowledgebase that can be practically useful for clinical work, was not within the scope of this work.
ST Algorithm for Medical Diagnostic Reasoning
TLDR
The required algorithm for medical expert system development involves a bottom-up and recursive process using logical inferences, abduction, deduction, and induction and can be identified and dealt with using the CLAP software process model.
Patient symptoms elicitation process for breast cancer medical expert systems: A semantic web and natural language parsing approach
Exploring Bayesian networks for medical decision support in breast cancer detection
TLDR
The researchers intend to design an interface between the project's Bayesian network learning algorithm and the radiologists, so that the Radiologists can have interaction with the system by labeling only a small number of informative images presented by the active learning algorithm.
Different Machine Learning Algorithms for Breast Cancer Diagnosis
TLDR
This study used the Wisconsin breast cancer dataset to compare five different learning algorithms, Bayesian Network, Naive Bayes, Decision trees J4.8, ADTree, and Multi-layer Neural Network along with t-test for the best algorithm in terms of prediction accuracy.
A process model of diagnostic reasoning in medicine
A Hybrid Artificial Immune Genetic Algorithm with Fuzzy Rules for Breast Cancer Diagnosis
TLDR
This paper introduces a hybrid algorithm that gathers the genetic algorithms with the artificial immune system in one algorithm and generates a fuzzy system which reached the maximum classification ratio earlier than the two other ones.
An epistemological framework for medical knowledge-based systems
TLDR
An abstraction paradigm for unifying different perspectives concerning the analysis and design of knowledge-based systems (KBSs) is presented and it is argued that the model provides a conceptual view on existing systems and some design insights for future ones.
On breast cancer detection: an application of machine learning algorithms on the wisconsin diagnostic dataset
TLDR
A comparison of six machine learning algorithms: GRU-SVM, Linear Regression, Multilayer Perceptron (MLP), Nearest Neighbor (NN) search, Softmaxregression, and Support Vector Machine on the Wisconsin Diagnostic Breast Cancer dataset by measuring their classification test accuracy, and their sensitivity and specificity values.
Generating concise and accurate classification rules for breast cancer diagnosis
  • R. Setiono
  • Computer Science
    Artif. Intell. Medicine
  • 2000
...
...