International Journal of Man-Machine Studies - Special Issue: Knowledge Acquisition for Knowledge-based Systems. Part 5
The KDD process for extracting useful knowledge from volumes of data
Communications of the ACM
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Machine Learning
Proceedings of the 3rd International Conference on Genetic Algorithms
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
A survey of Knowledge Discovery and Data Mining process models
The Knowledge Engineering Review
Medical data mining by fuzzy modeling with selected features
Artificial Intelligence in Medicine
Improved use of continuous attributes in C4.5
Journal of Artificial Intelligence Research
A scalable decision tree system and its application in pattern recognition and intrusion detection
Decision Support Systems
Expert Systems with Applications: An International Journal
Using Bayesian networks to analyze medical data
MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
Uniqueness of medical data mining
Artificial Intelligence in Medicine
Confidentiality issues for medical data miners
Artificial Intelligence in Medicine
Artificial Intelligence in Medicine
Evolutionary design of decision trees for medical application
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
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Accurate and early prediction of the outcome of an in vitro fertilization (IVF) treatment is important for both patients and physicians. The most common question asked by IVF patients is ''What are my chances of conceiving?'' The answer to this difficult question typically considers patient age, day 3 serum follicle stimulating hormone (FSH) levels, and infertility diagnosis. However, many more parameters are known to affect IVF outcome. It is difficult for the clinician to recognize trends and intuitively decide how to optimize success rates for each infertile couple. This paper presents a hybrid intelligence method which integrating genetic algorithm and decision learning techniques for knowledge mining of an IVF medical database. The proposed method can not only assist the IVF physician in predicting the IVF outcome, but also find useful knowledge that can help the IVF physician tailor the IVF treatment to the individual patient with the aim of improving the pregnancy success rate. The twenty-eight most significant attributes for determining the pregnancy rate (e.g., patient's age, number of embryo transferred, number of frozen embryos, and culture days of embryo) and their combinative relationships (represented by if-then rules) were identified through the proposed method. The knowledge discovered in this study is currently accepted as an interesting discovery from the viewpoint of domain experts. For the results from this study to be conveniently accessed by IVF physicians and patients, an expert system tool equipped with the proposed IVF outcome prediction model was built.